Container With Most Water
Identifying Problem Isomorphism
“Container With Most Water” can be mapped to “Trapping Rain Water”.
Both problems involve the concept of water storage and involve arrays. In “Container With Most Water”, you need to calculate the maximum area of water that can be contained between two lines, and in “Trapping Rain Water”, you need to calculate the maximum amount of water that can be trapped between bars or elevations.
“Container With Most Water” uses a twopointer approach where the area is calculated by considering the height and distance between the lines, “Trapping Rain Water” is more complex and uses dynamic programming. In “Trapping Rain Water”, you need to precompute the left maximum and right maximum for every bar to know how much water it can trap.
10 Prerequisite LeetCode Problems
“Container With Most Water” requires an understanding of arrays and the twopointer technique, as well as the ability to reason about space and optimization. Here are 10 problems to prepare:
Two Sum (LeetCode 1): This problem also requires you to find two numbers that meet a specific condition (their sum equals a target number). It’s a simpler version of the twopointer technique.
Reverse String (LeetCode 344): This problem is about reversing an array in place, which requires a grasp of how to manipulate arrays with two pointers.
Valid Palindrome (LeetCode 125): This is another problem that uses the twopointer technique, but this time it’s used to compare characters at opposite ends of a string.
Remove Duplicates from Sorted Array (LeetCode 26): This problem helps you understand the idea of maintaining a “fast” and “slow” pointer when iterating over an array.
Remove Element (LeetCode 27): This problem requires you to remove certain elements from an array, similar to manipulating the array in the “Container With Most Water” problem.
Max Consecutive Ones (LeetCode 485): This problem asks you to find the longest contiguous sequence of ones in a binary array, which helps you think about maintaining and calculating maximum lengths.
Find the Duplicate Number (LeetCode 287): In this problem, you use two pointers to find a cycle in an array, which improves your understanding of how pointers can traverse an array.
Two Pointers Squares of a Sorted Array (LeetCode 977): This problem requires you to square each number in a sorted array so that the resulting array is sorted, which is a good exercise in handling pointers at both ends of an array.
Longest Substring Without Repeating Characters (LeetCode 3): Here you’ll use a sliding window, a similar concept to the twopointer technique, to find the longest substring without repeating characters.
Sliding Window Maximum (LeetCode 239): This problem asks for the maximum in all contiguous subarrays of a certain size. It will teach you about how sliding windows can be used for optimization, which is crucial for the “Container With Most Water” problem.
These problems gradually increase in complexity and help you build up the techniques required for the “Container With Most Water” problem.
Problem Boundary
Establishing the boundaries of a problem means defining its limits or scope – identifying what is included within the problem space and what isn’t. In the context of this problem, we can establish the boundaries as follows:
Input Scope: The input to the problem is an array of integers, ‘height’, representing the heights of vertical lines. The length of this array, ’n’, is between 2 and 10^5 inclusive. Each height[i] is a nonnegative integer less than or equal to 10^4.
Problem Domain: The problem falls within the domain of computational geometry, optimization, and array manipulation. It doesn’t involve other data structures like linked lists, trees, or graphs, nor does it involve other areas like network communication or multithreading.
Solution Space: The solution needs to find two vertical lines from the array that, together with the xaxis, forms a container that can hold the maximum amount of water. We aren’t asked to find the positions of these lines, just the maximum volume of water.
Constraints: The vertical lines forming the container must be upright; we can’t slant them. The choice of lines should be from the given array, and we can’t change the heights or the positions of the lines.
Output: The output of the problem is a single integer that represents the maximum volume of water that can be contained.
External Factors: The problem doesn’t depend on any external factors or resources, such as network access, disk storage, user input, or time of day.
These boundaries help us to focus on the specific aspects we need to consider while solving the problem and ignore other potentially distracting factors.
Problem Classification
This problem falls into the category of computational geometry problems. Specifically, it involves finding the maximum area enclosed by the xaxis and any two lines from a given set of vertical lines. These lines are represented by the height array, where the value at each index represents the height of the line and its position.
‘What’ Components:
Integer Array (height): You’re given an integer array ‘height’ of length ’n’. Each element in this array represents the height of a vertical line, and the index represents its position on the xaxis.
Vertical Lines: There are ’n’ vertical lines drawn such that the two endpoints of the ‘ith’ line are at (i, 0) and (i, height[i]).
Container with Most Water: The task is to find two lines that together with the xaxis forms a container, such that the container can contain the most water.
Maximum Area: The goal is to return the maximum amount of water a container can store, which essentially means finding the maximum area under the curve formed by the lines and xaxis.
This problem can be classified as an optimization problem. The task is to optimize (maximize, in this case) the amount of water that can be stored between two lines. This involves evaluating multiple possible pairs of lines, thus falling into the subset of optimization problems known as “search problems”.
This also has aspects of dynamic programming (DP), in that an optimal solution involves making a sequence of decisions (which lines to choose), where the choice at each step depends on the choices made at previous steps.
It involves dealing with arrays and iterative access to their elements, which is a common feature in many problems related to data structures and algorithms.
The problem belongs to the domains of computational geometry, optimization (search problems), and data structures (arrays).
Clarification Questions
For this problem, you might want to ask the following clarification questions:
 Can there be lines with a height of zero?
 Is it guaranteed that the array of heights will have at least two elements?
 Do the two lines forming the container have to be adjacent?
 Are negative heights allowed for the lines?
 Is it possible to have multiple containers with the same maximum amount of water? If yes, should we return any or all of them?
 Is the array of heights sorted in any specific way, or is it random?
 Can the same line be used in more than one container?
 Is it allowed to have two lines of the same height?
These questions will help you better understand the constraints and requirements of the problem, making it easier to formulate a solution.
Identifying Problem Isomorphism
The problem “Container With Most Water” can be mapped to “Largest Rectangle in Histogram”.
In both problems, you’re trying to maximize an area using heights, albeit in different contexts. “Container With Most Water” aims to maximize the area of water trapped between two lines, while “Largest Rectangle in Histogram” aims to find the largest rectangle that fits within the histogram. The histogram problem is generally considered more complex due to additional conditions and the need to consider both height and width more explicitly.
Distilling the Problem to Its Core Elements
Fundamental Concept: This problem is primarily based on the concept of computational geometry, specifically dealing with the calculation of areas under a curve (or between lines in this case). The principle it employs is the optimization technique of twopointer approach, to efficiently traverse the array.
Simple Description: Imagine you have several vertical lines of different heights standing next to each other. You can choose any two lines and fill the space between them with water up to the height of the shorter line. Your task is to find which two lines can hold the most water.
Core Problem & Simplified Statement: The core problem here is to find the maximum area that can be formed between the vertical lines using the heights of the lines and the distance between them. A simplified problem statement could be: “Given the heights of several vertical lines, find the two lines that, together with the base, can contain the maximum amount of water.”
Key Components:
 Input array of heights.
 Two pointers to iterate through the array, representing the lines chosen to form the container.
 Calculation of the area between the two lines (which is determined by the height of the shorter line and the distance between the two lines).
Minimal Set of Operations:
 Initialize two pointers, one at the beginning and one at the end of the array.
 Calculate the area formed by the lines at the pointers and update the maximum area if this area is larger.
 Move the pointer pointing to the shorter line towards the other pointer.
 Repeat the above two steps until the pointers meet.
Visual Model of the Problem
To visualize this problem, think of the array’s elements as heights of vertical lines on a 2D plane, with the indices of the elements as the xcoordinates and the values as the ycoordinates (heights).
Suppose we have an array of [1,8,6,2,5,4,8,3,7]
. This can be visualized as:
8   
7    
6     
5      
4       
3        
2        
1          
0  
0 1 2 3 4 5 6 7 8
Now, imagine choosing two lines and filling the area between them up to the height of the shorter line with water. The aim is to find the pair of lines that can contain the most water.
For instance, if we choose the lines at indices 1 and 8 (both of height 8), we can visualize the container holding water as:
8  ~~~~~~~~~~~
7  ~~~~~~~~~~~ 
6  ~~~~~~~~~~~ 
5  ~~~~~~~~~~~ 
4  ~~~~~~~~~~~ 
3  ~~~~~~~~~~~ 
2  ~~~~~~~~~~~ 
1   ~~~~~~~~~~~  
0  
0 1 2 3 4 5 6 7 8
The tilde (~) symbol here is used to represent water. The area of water that can be held is determined by the height of the shorter line multiplied by the distance between the lines, which in this case is 7 * 8 = 56
. However, the max area is 49, which is formed between lines at index 1 and 8. We need to identify such a pair that would give us the maximum area.
Problem Restatement
You’re given an array of nonnegative integers, where each integer represents the height of a vertical line on a chart. The position of the line on the chart corresponds to its position in the array.
Your task is to find two lines such that the space between them (along the horizontal or xaxis), filled up to the height of the shorter line (along the vertical or yaxis), forms a container that can hold the maximum amount of water.
The catch is, you can’t tilt the container. This means the amount of water the container can hold is limited by the height of the shorter line, and the width of the container is the distance between the two lines.
You need to return the maximum volume of water that the container can hold. This volume is calculated as the distance between the two lines (width) multiplied by the height of the shorter line (height).
In terms of constraints:
 The array’s length is at least 2 and at most 10^5.
 The heights of the lines range from 0 to 10^4.
Abstract Representation of the Problem
Here’s a more abstract representation of the problem:
You have a list of positive integers. Each integer represents a point on a coordinate plane, with its index in the list corresponding to its xcoordinate and its value corresponding to its ycoordinate.
Your goal is to select two points that create a rectangle with the xaxis that has the maximum possible area. The rectangle’s height is defined by the lower of the two ycoordinates, and its width is the difference between the two xcoordinates. The area is defined as width multiplied by height.
Your task is to find the maximum possible area of such a rectangle.
Constraints:
 The list’s length ranges between 2 and 10^5.
 The values in the list range from 0 to 10^4.
In an interview context you have to ask questions and reformulate the problem that can be solved.
Terminology
Array (or List): This is a fundamental data structure in computer science that organizes items in memory sequentially. In the context of this problem, the array represents the heights of the lines.
Area of Rectangle: This is a basic concept in geometry. The area of a rectangle is calculated by multiplying its length by its width. In the context of this problem, the width is the difference between the indices (xcoordinates) of two elements in the array and the height is the minimum value of these two elements (ycoordinates).
TwoPointer Technique: This is a common strategy in computer science for traversing sequences in an efficient way. Usually, two pointers start at different positions (commonly one at the start and one at the end) and move towards each other. In the context of this problem, the twopointer technique can be used to efficiently find the pair of lines that forms the container with the most water.
Optimization Problem: This is a type of problem that involves finding the best solution from all possible solutions. In this case, the task is to maximize the area of the water container, which is an optimization problem.
Understanding these concepts is crucial to comprehend and solve this problem. They help define the problem space and constraints, as well as provide tools and strategies for finding a solution.
Problem Simplification and Explanation
Let’s simplify the problem and relate it to a more familiar scenario.
Imagine you have a row of buckets of varying heights arranged from left to right. Each bucket’s height corresponds to an element in the given array. Now, you need to water your garden, but all you have is a flat, rigid board to carry water from these buckets to your garden.
You can place this board on top of any two buckets such that it forms a sort of container with the two chosen buckets acting as the sides. However, the board cannot be tilted or slanted, it has to remain horizontal. The amount of water this makeshift container can carry is limited by the height of the shorter bucket (as water would spill over the shorter side) and the distance between the two buckets (as the board’s length).
Your goal is to choose two buckets that can hold the maximum amount of water on the board, i.e., maximize the area of the “container”. You measure the distance between buckets in bucket widths (akin to array indices) and the bucket’s height is its height (the array values).
In more technical terms, you are trying to maximize the area of a container, where the length is the distance between the two elements in the array, and the height is the minimum value of these two elements.
Key concepts here include understanding the array values as heights, the array indices as distances, and the problem itself as an optimization task, where you are trying to maximize the area formed by two array elements and the distance between them.
Constraints
Given the problem statement and the constraints, there are a few characteristics that we can exploit to find an efficient solution:
Twopointer Technique: Since the problem deals with an array (or a list of heights in this case), a common technique that could be applied here is the twopointer technique. We start with two pointers, one at the start of the array and one at the end. This allows us to consider the widest possible container first, and then we can adjust our pointers to try and find a higher container.
Decreasing Distance, Increasing Height: The distance between the two lines decreases as we move our pointers inward, so in order to find a larger area, we need to find a significantly larger height. Therefore, it makes sense to move the pointer that is currently pointing to the shorter line, because moving the pointer at the taller line wouldn’t increase the minimum height between the two lines.
Array Values Range: The height of each line is a nonnegative integer. This fact could potentially be exploited in certain solutions.
Array Length Constraints: The constraints specify that the array length is relatively large (up to 10^5). This suggests that a solution with a time complexity of O(n^2) would be too slow, guiding us toward linear time complexity solutions.
Pattern in the Optimal Solution: The optimal solution must involve two lines, where the height of the shorter line is maximized, and the distance between the lines is as large as possible. This is the crux of the problem and the pattern we’re looking to exploit.
In summary, the properties of the array structure, the constraints on the array length, and the need to maximize the area (min(height) * width) are characteristics that can be exploited to guide our approach to an efficient solution.
Analyzing the constraints can help us form some key insights:
Size of the array: The size of the array can be as large as 10^5 elements. This tells us that the solution must be efficient. A solution that requires considering every pair of heights (a brute force solution) would have a time complexity of O(n^2), which will be too slow for larger inputs. Therefore, we need to come up with an approach that ideally has a linear time complexity, i.e., O(n).
Range of heights: The height of each line is a nonnegative integer and can be as large as 10^4. However, the height of the container is determined by the shorter of the two lines, so just having a tall line doesn’t necessarily mean that the container can hold more water. This points us towards a solution where we have to consider both the height of the lines and the distance between them to maximize the area.
Two lines form a container: The problem states that the container is formed by two lines and the xaxis. This suggests that we should be considering pairs of lines, which points towards a solution involving two pointers.
No slanting: The container cannot be slanted, which means the water it holds will always be limited by the height of the shorter line. This leads to the insight that to maximize the water a container can hold, we should try to maximize the height of the shorter line, while also keeping the lines as far apart as possible.
By analyzing the constraints, we can conclude that we need an efficient solution (due to the large possible array size) that considers both the height of the lines and their separation. A twopointer approach seems to be a good way to handle these requirements.
Case Analysis
Let’s examine several different cases, which will help us further understand this problem.
Minimal Case:
 Input: height = [1, 1]
 Output: 1
 Reasoning: Here we have the smallest possible input where we have only two vertical lines of equal height. The maximum area of water the container can contain is height * width = 1 * 1 = 1.
All Equal Heights:
 Input: height = [2, 2, 2, 2, 2]
 Output: 8
 Reasoning: In this case, all the vertical lines have equal height. So the maximum area of water the container can contain is max_height * max_width = 2 * 4 = 8.
All Different Heights (Ascending Order):
 Input: height = [1, 2, 3, 4, 5]
 Output: 6
 Reasoning: Here, the maximum area of water the container can contain is formed by the first and the last vertical line. Because even though the last line is the highest, the area is limited by the shorter line. So, the maximum area = min(height_first, height_last) * width = 1 * 4 = 4.
All Different Heights (Descending Order):
 Input: height = [5, 4, 3, 2, 1]
 Output: 6
 Reasoning: Similar to the previous case, the maximum area of water the container can contain is formed by the first and the last vertical line. The maximum area = min(height_first, height_last) * width = 1 * 4 = 4.
Mixed Heights:
 Input: height = [1,8,6,2,5,4,8,3,7]
 Output: 49
 Reasoning: Here, the maximum area of water is not formed by the tallest lines but by the lines at second and last position. The maximum area is limited by the shorter of the two lines, and also we need to consider the width, so the maximum area = min(8, 7) * width = 7 * 7 = 49.
Large Input Case:
 Input: height = [1]*10^5
 Output: 10^5
 Reasoning: In this case, we have a large input where all the heights are equal. So the maximum area of water the container can contain is max_height * max_width = 1 * 10^5 = 10^5.
These cases should give us a good understanding of how the problem works and help ensure that our solution covers all possible edge and boundary conditions.
Analyzing these different cases brings out several key insights:
Shorter line dictates the area: The area that can be filled with water is determined by the shorter of the two lines, not the taller one. This is because the water level is limited by the lower line.
Width matters: The distance between the two lines (i.e., the width of the container) is an essential factor in the calculation of the area. The wider the distance, the greater the potential area, even if the limiting line is not the tallest.
Tallest doesn’t always mean largest area: Even if some lines are taller, they may not contribute to the maximum possible area. In the ‘Mixed Heights’ case, even though there were lines taller than the ones at second and last position, they didn’t result in the maximum area because they were not widely spaced.
All equal heights: If all the lines are of equal height, then the widest container will have the maximum area.
Decreasing or increasing heights: In cases where the heights are in increasing or decreasing order, the maximum area is obtained by the widest container, i.e., by choosing the first and the last line.
Large input: Even with a large number of lines, the problem can be solved by applying the same principles. The maximum area will be the product of the height of the lines and the width of the container.
Small input: For the smallest possible input, where there are only two lines, the maximum area is simply the height of the line (since both lines are of equal height) times the width (which is 1).
These insights will guide us in forming our approach to solving the problem.
Identification of Applicable Theoretical Concepts
Several mathematical and algorithmic concepts apply to this problem that make it more manageable:
Area Calculation: The area that a pair of lines can hold is determined by the product of the height of the shorter line and the distance between the two lines. This is a simple mathematical concept that’s crucial for this problem.
Twopointer Technique: This problem can be solved effectively using a Twopointer technique, which is an algorithm design pattern often used for searching pairs in a sorted array. The pointers start at two ends and move towards each other, this movement is based on some condition. In this problem, the condition is based on the height of the lines.
Greedy Approach: The greedy approach is a strategy that makes the locally optimal choice at each step with the hope that these local choices will lead to a global optimum. Here, we make the optimal choice at each step based on the height of the lines and the width between them to find the maximum area.
Optimization Problem: This problem is an example of an optimization problem, which is a common category of problems in computer science and mathematics. In optimization problems, the goal is to find the best solution from all possible solutions. Here, we need to find the combination of lines that give the maximum area.
By applying these concepts, we can simplify the problem and make it more manageable to solve.
Simple Explanation
It’s like having a row of cups of different heights arranged randomly, and you’re asked to find two cups among them such that when you connect the top edges with a flat board and fill them with water, they can hold the maximum amount of water. The only condition is that the water level will be determined by the shorter cup, and you cannot tilt any of the cups.
Imagine you’re at the beach, and you’ve dug many holes in the sand with varying depths. Now you have a flat piece of wood and you want to cover two holes with it to store the most water from an incoming wave. The amount of water you can store is limited by the depth of the shallower hole and the distance between the two holes. Your task is to figure out which two holes to cover.
Let’s say you have several building blocks of different heights, and you want to build a swimming pool using two of these blocks. The blocks will be the walls of your pool, and you can use a flat board as the base. But, the depth of your pool can only be as deep as the shorter block, and the length of your pool will be the distance between the two blocks. Your task is to choose the best two blocks to create the biggest swimming pool.
Think about having several buckets of different sizes, and you have a big, flat tray. You want to place two buckets under the tray to collect rainwater. The amount of rainwater collected will be limited by the shorter bucket, and the area of the tray you can cover is the distance between the two buckets. You need to figure out which two buckets would collect the most rainwater.
Consider you’re setting up a limbo bar for a game. You have sticks of different heights that you can use as the side supports. The bar will always be placed on the shorter stick, and the distance between the two sticks will be the length of the bar. Your challenge is to pick two sticks that will allow the bar to be at the optimum height and length for the game.
Problem Breakdown and Solution Methodology
The Container With Most Water problem can be likened to a scenario where you have several vertical planks of different heights lined up in a row, and you want to pick two such that you can hold the maximum amount of water between them.
Here’s how we can approach solving this problem:
Initialize Two Pointers: We start by placing two pointers at both ends of the array. These pointers represent the two planks we’ve chosen to form our container. This is like standing at both ends of the row of planks and considering the container that would be formed by the two end planks.
Calculate Area: Calculate the area of water that can be stored between the two planks. The area would be the distance between the two planks (i.e., the index difference) multiplied by the height of the shorter plank. This is because the amount of water a container can hold is determined by the height of the shorter plank (height) and the distance between them (width).
Move Pointers: The idea is to try to increase the area by moving the pointers. We always move the pointer pointing to the shorter plank, because moving the pointer pointing to the taller plank wouldn’t increase the height of the container, thus it would only decrease the width and consequently, the area.
Iterate: We repeat steps 2 and 3, constantly calculating the area and moving the pointers until the pointers meet. Every time we calculate an area, we check if it’s larger than our previously stored maximum area and update it if it is.
Result: The maximum area we’ve stored during this process will be the maximum amount of water that can be stored.
For example, consider the height array [1,8,6,2,5,4,8,3,7]. Initially, the pointers are at the first and last planks (heights 1 and 7). The area is min(1,7)(91) = 8. Moving the left pointer (since height 1 < height 7), the next pair is (8,7) with area min(8,7)(81) = 56. Proceeding this way, we find the maximum area is 49 for the pair (8,7) at indices 2 and 9.
Modifying the problem’s parameters like the height of the planks or their arrangement would affect the maximum area that can be stored. More planks or higher planks generally mean more possibilities for larger areas.
Inference of ProblemSolving Approach from the Problem Statement
Here are the key terms and concepts in this problem:
Container: The problem is about finding a container that can hold the most water. A container in this context is formed by two lines in the array and the space between them. This guides us to consider pairs of lines in the array.
Lines: The lines are represented by integers in the array, where the value of the integer denotes the height of the line. This suggests that the value of elements in the array will play a role in determining the volume of water that can be stored.
Most Water: We are trying to maximize the amount of water the container can hold. This is essentially an optimization problem, suggesting strategies like dynamic programming or greedy algorithms may be applicable.
Form a Container: Two lines together with the xaxis form a container. The “container” here is basically an area with the xaxis as its base. The problem thus translates to finding two lines that can form the maximum area with the xaxis.
xaxis: This refers to the indices of the array or the distance between two lines. As we’re dealing with areas, the distance between two lines (the width of the container) will affect the total area and needs to be considered in the solution.
Height[i]: Represents the height of the vertical lines. The minimum height between two lines will determine the height of the water container, as water will overflow from the shorter line.
By analyzing these key concepts, we understand that we need to consider all pairs of lines, calculate the area they form, and keep track of the maximum area found. Due to the pairwise nature of the problem and the need to maximize the area, a twopointer approach moving from the outermost lines towards the center seems suitable. This is because we start with the maximum width and then try to improve the height, which makes it a form of greedy strategy.
The problem “Container With Most Water” has a property where we are trying to find two lines out of an array that together with the xaxis forms a container such that it contains the most water. The container’s capacity is determined by two factors  the distance between the two lines (i.e., their indices) and the height of the shorter line.
If we start from the ends of the array and move inward, we’re effectively decreasing the distance between the two lines, so we want to ensure the possibility of finding a taller line that could potentially hold more water. This is where the twopointer strategy comes in  we use one pointer at the beginning of the array and one at the end and gradually move them towards each other.
There are two key insights that lead us to this approach:
The width of the container decreases as we move towards the center: Starting from the widest possible container (i.e., using the first and the last line) means any other container we consider will be less wide, so we can only hope to improve the height.
The amount of water a container can hold is determined by the shorter line: When we have two pointers, one at the start and one at the end, if we move the pointer at the taller line, we can’t possibly get a better container because we’re reducing the width and can’t increase the height. However, if we move the pointer at the shorter line, we have a chance of increasing the height, and thus, possibly finding a better container.
These insights suggest that we can iterate over the array only once instead of considering all pairs of lines (which would take quadratic time), making the problem much more tractable. That’s how we infer that the twopointer strategy could be an efficient solution for this problem.
Stepwise Refinement
Here’s a stepbystep breakdown of the twopointer approach to solve the “Container With Most Water” problem:
Initial Setup: We initialize two pointers,
left
andright
, at the beginning and end of the array, respectively. We also setmax_area
to 0, which will keep track of the maximum area of water that we can trap.Calculate Area: Calculate the area between the lines at the
left
andright
pointers. The height of the container is the minimum height between the two lines, and the width is the distance between them. So,area = min(height[left], height[right]) * (right  left)
. If thisarea
is greater thanmax_area
, then we updatemax_area
.Move Pointers: If the height at the
left
pointer is less than or equal to the height at theright
pointer, we move theleft
pointer one step towards the right (i.e.,left = left + 1
). Otherwise, we move theright
pointer one step towards the left (i.e.,right = right  1
). The rationale behind this is that we always want to try and increase the height of the shorter line in the hopes of finding a container with a larger area.Repeat: We repeat steps 2 and 3 until the
left
pointer is not less than theright
pointer.Result: At the end of this process,
max_area
will hold the maximum area of water that can be trapped according to the heights of the lines and the distance between them.
The reason we move the pointer at the shorter line in step 3 is that moving the pointer at the taller line can’t possibly get a better container. We’re reducing the width and can’t increase the height. However, by moving the pointer at the shorter line, we have a chance of increasing the height and, therefore, possibly finding a better container.
Remember that we’re always choosing the maximum area we’ve seen so far. This ensures that even if we decrease the width by moving the pointers inward, we’re always checking if we can find a better area with potentially taller lines.
Let’s break down the twopointer approach to the “Container With Most Water” problem into even more granular, actionable steps:
Define Variables: Start by defining the
left
pointer at position 0, theright
pointer at the last position of the array (positionn1
wheren
is the length of the array), and a variablemax_area
to keep track of the maximum water that can be trapped.Check Validity: Check that the
left
pointer is less than theright
pointer. If not, the calculation of area is not valid.Calculate Area: While
left
pointer is less than theright
pointer, calculate the area of water that can be trapped between the vertical lines pointed to byleft
andright
. This area is calculated as:area = min(height[left], height[right]) * (right  left)
Update Max Area: Compare the newly calculated
area
with the currentmax_area
. Ifarea
is greater thanmax_area
, updatemax_area
to be equal toarea
.Update Pointers: Determine which pointer to move. If the height at the
left
pointer is less than or equal to the height at theright
pointer, increment theleft
pointer by one. Otherwise, decrement theright
pointer by one. The idea is to try and increase the height of the container by moving the pointer that is at the shorter line.Repeat Steps: Repeat steps 2 to 5 until the
left
pointer is no longer less than theright
pointer.Final Result: After the pointers have met,
max_area
will contain the maximum area of water that can be trapped. This is the final result.
This approach works because by moving the pointer at the shorter line, we’re trying to increase the height of the container while sacrificing some width. Even though the width decreases, we have the chance of finding a taller height that might lead to a larger area.
This problem is not easily divisible into independent parts due to its inherent sequential nature. Solving it requires us to take into account the relative positions and heights of each pair of lines. Therefore, the process of calculating the maximum area depends on the results of previous calculations and is inherently dependent on them.
However, there are subtasks in the problem that can be considered individually for the sake of understanding, although they cannot be solved entirely independently. These include:
Initialization: The initialization of the two pointers, left and right, and the maximum area, max_area, is independent of the other steps and serves as the setup for the problem.
Area Calculation: Once left and right pointers are given, the calculation of the area between two lines is independent of the overall algorithm. The calculation,
min(height[left], height[right]) * (right  left)
, can be performed irrespective of the other operations.Pointer Update: The update rule for the pointers can be defined independently of the area calculation. If the height at the
left
pointer is less than or equal to the height at theright
pointer, increment theleft
pointer by one. Otherwise, decrement theright
pointer by one.Max Area Update: The updating of max_area based on the newly calculated area can also be defined independently. If the newly calculated area is greater than the current max_area, then update max_area.
So, while these subtasks can’t be solved entirely separately, understanding them as individual components can make the problem easier to grasp and solve.
There are several repeatable patterns within our solution:
Area Calculation: The calculation of the area between the two lines is a repeatable operation that we perform in every iteration of the loop. We calculate the area as the distance between the two pointers (lines) multiplied by the minimum height of the two lines, i.e.,
min(height[left], height[right]) * (right  left)
.Pointer Adjustment: In each iteration, we adjust one of the pointers (either
left
orright
). We decide which pointer to adjust based on a comparison of the heights at the current positions of theleft
andright
pointers. Ifheight[left]
is less than or equal toheight[right]
, we increment theleft
pointer. Otherwise, we decrement theright
pointer. This adjustment pattern repeats until the two pointers meet.Max Area Update: The process of updating the maximum area (if the calculated area in the current iteration is greater than the existing maximum area) is also a repeating pattern.
These repeating patterns form the core loop of our algorithm, and understanding them is key to comprehending how the solution works.
Solution Approach and Analysis
Let’s break down the problemsolving process and explain it with a metaphor. Let’s imagine the given array of heights as a range of hills with varying altitudes.
Initialization: We start by placing two mountaineers at either end of the range, ready to trek towards each other. At this point, our largest ‘container’ of water is still empty.
Calculate Area & Update Maximum: We calculate the area of the ‘valley’ between the two mountaineers by taking the minimum height between the two (as the water will overflow from the lower side) and multiplying it by the distance between them (width of the container). If this area is larger than our current maximum, we update the maximum.
Adjust Position: To increase the potential for a larger ‘valley’, the mountaineer at the lower hill decides to move. The logic is simple: moving the mountaineer at the higher hill won’t increase the minimum height, but moving the one at the lower hill might. So, the mountaineer at the lower hill treks one step towards the other.
Repeat Steps 2 & 3: We repeat the process of calculating areas, updating the maximum, and adjusting positions until the two mountaineers meet.
Result: At the end, the largest ‘valley’ (or waterfilled container) we found during these steps is our answer.
Let’s now apply this method to an example case. Let’s take the array [1,8,6,2,5,4,8,3,7].
 Start with two pointers at either end, pointing to 1 and 7 respectively. The area of the water container is min(1,7) * 8 = 8. This is our maximum area so far.
 The left pointer is at the lower height, so it moves one step to the right. Now the left pointer is at 8 and the right pointer is still at 7. The new area is min(8,7) * 7 = 49. This is larger than our previous maximum, so we update our maximum area to 49.
 We keep repeating this process. Once the two pointers meet, we return the maximum area found, which in this case is 49.
The algorithm’s performance depends heavily on the distribution of heights. However, since we’re iterating through the array only once, the time complexity remains O(n) for all scenarios, where n is the size of the height array. The space complexity is O(1) as we’re only using a constant amount of space for the pointers and the max_area variable.
Identify Invariant
In the context of the “Container With Most Water” problem, the invariant can be expressed as follows:
The maximum area that can be formed between the two lines (or points) on the left and the right, inclusive, will always be the maximum possible area within the remaining unexplored subarray.
This invariant remains true throughout the execution of the algorithm. We maintain this invariant by always moving the pointer pointing to the shorter line towards the other pointer. By doing this, we are effectively ruling out the shorter line from potential maximum area contenders because any area with the shorter line would necessarily be less than the area we just calculated.
So, when we move the pointer to a new position, we are only considering areas with the line at this new position, and lines we haven’t considered yet. This ensures that the invariant is maintained throughout the algorithm execution, and ultimately leads us to the maximum possible area.
Identify Loop Invariant
In the “Container With Most Water” problem, the loop invariant can be described as follows:
At any point during the execution of the twopointer approach, the maximum area that can be formed by the lines to the left and right of the current positions, inclusive, is the maximum area obtainable within the unexplored portion of the array.
This loop invariant holds at the beginning of each iteration. Here’s how we maintain it:
Initially, the pointers are at the two ends of the array, ensuring the invariant is true at the start. As we move inwards, we always advance the pointer at the shorter line, which effectively removes the shorter line from consideration for the maximum area because any area it could form would necessarily be less than the area just computed.
Hence, after each iteration, the invariant is maintained, leading us towards the maximum possible area.
The terms “invariant” and “loop invariant” can often be used interchangeably in the context of a programming problem.
However, the term “invariant” generally refers to a condition that remains true before and after each iteration in a sequence of operations. On the other hand, a “loop invariant” specifically refers to a condition that remains unaltered before and after each iteration of a loop.
In the context of the “Container With Most Water” problem, since we are discussing the property within the context of the twopointer approach loop, it’s accurate to call it a “loop invariant”. The invariant — the fact that the maximum area is always the maximum obtainable within the unexplored portion of the array — holds before, during, and after the loop execution. Therefore, in this specific case, the terms “invariant” and “loop invariant” essentially refer to the same concept.
Thought Process
Solving the “Container With Most Water” problem requires an understanding of the twopointer technique in array traversal. The thought process involved in this problem includes the following steps:
Understanding the Problem: The first step is to make sure we understand the problem. We are given a nonnegative integer array
height
, where each integer represents the height of a line on the coordinate axis. The area of water any two lines can contain depends on the distance between them and the height of the shorter line. We are asked to find the maximum area that can be contained by any two lines.Key Insights from Problem Statement: The problem statement suggests that we need to maximize the area of water between two lines, which is the product of the distance between the lines and the height of the shorter one. This hints that we need to consider both the height and the position of the lines while calculating the area. The “maximize” requirement suggests an optimization problem, which could often be solved by Greedy algorithms, DivideandConquer, or Dynamic Programming.
ProblemSolving Approach: Since the number of lines and their possible pairings are quite large, a bruteforce approach that checks every possible pair of lines would be inefficient (O(n^2) time complexity). So we need to think of a more efficient approach.
A key observation here is that if we start from the widest possible container (i.e., the outermost pair of lines), and move the taller line among them towards the center, the width of the container decreases, and the height cannot increase, thereby the maximum area can never increase. On the other hand, if we move the shorter line towards the center, there’s a chance that we might come across a line which is longer, and potentially increases the maximum area.
This insight leads to the twopointer approach: start with two pointers at the ends of the array, calculate the area, and then move the pointer pointing at the shorter line towards the other one. Repeat this process until the two pointers meet.
Here is the Python code that implements this approach:


This code has a time complexity of O(n) and a space complexity of O(1), where n is the length of the height
array.
Establishing Preconditions and Postconditions
Problem Name:
 The problem we want to solve is called “Container With Most Water”.
Method Name:
 The name of the function that we are using is
maxArea
.
 The name of the function that we are using is
Parameters:
 The input to the method is
height
. height
is a list of integers. In the context of the problem,
height
represents the heights of the lines. Each integer in the list represents the height of a vertical line drawn on a 2dimensional plane.
 The input to the method is
Preconditions:
 Before this method is called, the
height
list must not be empty and it should contain at least two elements as there must be at least two lines to form a container. Each element in theheight
list must be a nonnegative integer.  There are no specific constraints on the input parameters, except that the length of the list should be in the range [2, 10^5], and each height should be in the range [0, 10^4].
 There’s no specific state that the program must be in.
 Before this method is called, the
Method Functionality:
 This method is expected to find the two lines that together with the xaxis forms a container, such that the container contains the most water. It calculates this by iterating through the list of heights, using two pointers to keep track of the currently considered pair of lines, and updating the maximum area whenever a larger area is found.
 It interacts with the inputs by reading the heights of the lines from the
height
list.
Postconditions:
 After the method has been called and has returned, the state of the program or the values of the parameters remains unchanged as the function does not modify the input list.
 The return value represents the maximum area of water that can be contained between any two lines.
 The method does not have any side effects.
Error Handling:
 The method does not include any explicit error handling. If the preconditions are not met, i.e., if the
height
list is empty or contains less than two elements, a Python error would likely occur during the execution of the method.  It does not throw an exception, return a special value, or do anything else in case of invalid inputs. In production code, it would be beneficial to add error handling code to validate the input and raise appropriate exceptions when the input is invalid.
 The method does not include any explicit error handling. If the preconditions are not met, i.e., if the
Problem Decomposition
Problem Name:
 The complex problem we want to solve is finding the two lines that together with the xaxis forms a container which can contain the maximum amount of water. This problem is popularly known as the “Container With Most Water”.
Problem Understanding:
 We are given a list of nonnegative integers representing the heights of vertical lines on a 2dimensional plane. Each line is positioned at a different, equallyspaced xcoordinate. We need to identify two lines such that, along with the xaxis, they form a container which can contain the maximum amount of water. The volume of water that can be contained is determined by the shorter of the two lines and the distance between them.
Initial Breakdown:
 This problem can be broadly divided into two subproblems:
 Iterating over all pairs of lines to calculate the water volume that each pair can contain.
 Keeping track of the maximum volume encountered while iterating over the pairs.
 This problem can be broadly divided into two subproblems:
Subproblem Refinement:
 The first subproblem (iterating over pairs) can be further broken down into:
 Initializing two pointers at the start and end of the array.
 Moving the pointers towards each other until they meet, calculating the water volume at each step.
 The second subproblem (keeping track of maximum volume) involves updating a variable if the calculated volume for a pair is greater than the current maximum volume.
 The first subproblem (iterating over pairs) can be further broken down into:
Task Identification:
 The repeated task in this problem is the calculation of water volume for a pair of lines and the comparison of this volume with the current maximum volume.
Task Abstraction:
 The tasks are abstracted at an appropriate level where they are clear, make sense in the context of the problem, and can be easily implemented in code.
Method Naming:
 The method for calculating the water volume could be named
calculate_volume
, and the method for keeping track of the maximum volume could be namedupdate_max_volume
.
 The method for calculating the water volume could be named
Subproblem Interactions:
 The two subproblems interact closely with each other: as we iterate over the pairs of lines, we continuously update the maximum volume if necessary. These two operations are repeated until all pairs have been considered. The iteration needs to be performed first, followed by the maximum volume update. The update of maximum volume depends on the calculated volume for the current pair of lines.
From Brute Force to Optimal Solution
Brute Force Approach:
A simple way to solve the problem is to consider all possible pairs of lines and compute the area of water they can hold. For each pair of indices (i, j), we calculate the area as min(height[i], height[j]) * (j  i)
, and keep track of the maximum area encountered.
Here is a Python code that does this:


This approach is straightforward, but it’s not efficient. The time complexity is O(n^2) because in the worst case, we are checking all pairs of lines.
Optimized Approach: Two Pointer Technique
Instead of checking all pairs of lines, we can make use of the twopointer technique to only consider pairs of lines that could potentially give us a larger area.
Here are the steps involved in this optimized approach:
 We initialize two pointers, one at the beginning of the array and one at the end.
 At each step, we calculate the area between the lines pointed to by the two pointers, update our maximum area if necessary, and move the pointer pointing to the shorter line towards the other pointer.
 We continue this process until the two pointers meet.
The intuition behind this approach is that by moving the pointer of the shorter line, we are hoping to find a longer line that can result in a larger area. Moving the pointer of the longer line won’t help because it won’t change the limiting shorter line and will reduce the width between the lines, thus reducing the potential area.
Here is the Python code for this approach:


This approach only requires one pass through the list of lines, so the time complexity is reduced to O(n), where n is the number of lines. The space complexity is O(1) as we are only using a constant amount of space to store our pointers and max_area.
So by moving from a brute force solution to an optimized twopointer technique, we have greatly improved the efficiency of our solution.
Code Explanation and Design Decisions
Initial parameters: The function maxArea
takes as input a list of height
representing the height of each line. The index in the array represents the position of the line on the xaxis. The initial parameters left
and right
are pointers initialized to the start and end of the height
list. The max_area
is initialized to 0 and keeps track of the maximum area encountered so far.
Primary loop: The primary loop iterates while left < right
, meaning that it will continue as long as there are at least two lines between which water could be contained. Each iteration represents a comparison between two lines, one pointed by left
and another by right
, to calculate the area of water that can be held between them.
Conditions within the loop: The condition if height[left] < height[right]
checks which line is shorter. This is important because the shorter line determines the maximum depth of water that can be contained between the two lines. If the left line is shorter, then the left
pointer is incremented, else the right
pointer is decremented.
Updates to parameters: At each iteration, the area is calculated and compared to the current max_area
. If the calculated area is larger, max_area
is updated. This reflects the continuous search for a larger area. As mentioned, the left
and right
pointers are also updated depending on which line is shorter to possibly find a larger area in the next iterations.
Invariant: The invariant in this code is that at each step, the max_area
contains the largest area of water that can be held between any pair of lines checked so far. This is important as our objective is to find the maximum area possible.
Final output: The final output is the max_area
, which represents the maximum amount of water that can be contained between any pair of lines. It is the solution to our problem and satisfies the problem’s requirements as it is the result of checking all possible valid pairs of lines due to the twopointer approach.
Coding Constructs
Highlevel problemsolving strategies: This code uses a strategy called the “twopointer technique” and optimizes the process of solving the problem by avoiding unnecessary computations. It also leverages the property of the problem domain where the amount of water that can be contained is determined by the shorter line.
Explaining the code to a nonprogrammer: This code is like two people standing at both ends of a row of differentheight fences and wanting to find out between which two fences they can hold the most water. The person at the shorter fence always moves towards the other person, because moving the taller fence inward wouldn’t help find a larger water container (since the water level is capped by the shorter fence), and there’s a chance to find a taller fence by moving the shorter one.
Logical elements used: The logical constructs used include a while loop (for repeating actions until a condition is met), conditional statements (for making decisions based on certain conditions), and variables (for storing and updating information).
Algorithmic approach in plain English: Start by standing at both ends (the start and end of the list). Then, calculate the area between these two points and keep a record if it’s the largest area you’ve found so far. Look at the heights at both ends, and move the pointer inward from the shorter height (since the shorter one limits the area). Repeat this process until the two pointers meet.
Key steps or operations on the input data: The steps include calculating the area of water that can be contained between the two lines pointed by left and right pointers, comparing this with the current maximum area, and updating it if necessary. The ’left’ or ‘right’ pointer is also updated at each step, moving inward from the side with the shorter line. The purpose of these steps is to systematically check all pairs of lines to find the maximum area of water that can be contained.
Algorithmic patterns or strategies: The twopointer technique is the key pattern used here. The two pointers move towards each other based on certain conditions, systematically reducing the search space. This strategy, combined with the understanding of the problem constraints (the shorter line determines the area), allows for an efficient solution.
Language Agnostic Coding Drills
Dissecting the code into distinct concepts:
Variable Initialization: This is where we define and initialize variables that will be used throughout the code. For this problem, this includes the left and right pointers, and the max_area.
Looping construct (While loop): A loop is a way to repeatedly execute a block of code. The while loop used here allows us to perform operations as long as the two pointers haven’t met in the middle.
Conditional Statements (ifelse): These are used to make decisions in code based on certain conditions. In this problem, we use a conditional statement to decide which pointer to move.
Calculation of the area and its comparison: A critical step in the problem, where we calculate the potential maximum area between two points and compare it with our current maximum area.
Updating Variables: The process of changing the value of a variable. In this context, it’s moving the left or right pointer towards the center, and updating the maximum area found so far.
Concepts ordered by increasing difficulty with descriptions:
Variable Initialization: The easiest step as it just involves declaring variables and assigning them initial values.
Calculation of area and comparison: This involves basic arithmetic and comparison operations, which are slightly more complex than simple variable initialization, but still pretty straightforward.
Updating Variables: The difficulty level rises here because the updating process requires careful consideration of conditions derived from the problem statement.
Conditional Statements (ifelse): This can be somewhat complex, as making the right decision based on a condition requires understanding of the problem domain. It requires understanding of the concept of branching in code.
Looping construct (While loop): This is the most complex concept because it requires understanding of iterative processes, managing exit conditions, and ensuring the loop behaves as expected.
Problemsolving approach using these concepts:
Start with the initialization of variables, specifically the two pointers at the ends and the variable to hold the maximum area. Then, enter the looping construct where the main computation will happen. The loop continues until the two pointers meet.
Within the loop, calculate the area between the two points identified by the pointers. This area is computed by finding the minimum height between the two points multiplied by the distance between them. Compare this calculated area with the current maximum area. If the calculated area is greater, update the maximum area.
Then, the condition comes into play. Ifelse conditions are used to decide which pointer to move. We move the pointer that’s at the shorter height because the limiting factor for the area is the height of the shorter line. This process continues until the pointers meet, at which point we have checked all possible pairs of lines. Once the loop finishes, we have the maximum area that can be contained, solving the problem.
Targeted Drills in Python
1. Coding Drills for Each Concept
Variable Initialization:
1 2 3
left = 0 right = 10 max_area = 0
Looping Construct (While loop):
1 2 3
while left < right: print(left, right) left += 1
Conditional Statements (ifelse):
1 2 3 4
if left < right: print("Left is smaller than Right") else: print("Right is smaller or equal to Left")
Calculation of the Area and Comparison:
1 2 3 4
height = [1, 2, 3, 4, 5] area = min(height[left], height[right]) * (right  left) if area > max_area: max_area = area
Updating Variables:
1 2 3 4
if height[left] < height[right]: left += 1 else: right = 1
2. ProblemSpecific Concept
Calculating the area between two lines in a 2D space
1 2 3 4
height = [1, 2, 3, 4, 5] left = 0 right = len(height)  1 area = min(height[left], height[right]) * (right  left)
This is an essential concept for this problem because the whole problem revolves around finding two lines which, together with the xaxis, forms a container that can hold the most water.
3. Integrating These Concepts
Once we understand these basic concepts and drills, we can combine them in a certain order to solve the initial problem. The process to integrate these drills together would look something like this:
 Start with initializing the variables. This sets up our starting state.
 Then, enter the while loop. This loop forms the backbone of our solution. Inside the loop, we perform the following operations:
 Calculate the area between the two lines pointed by the
left
andright
pointers.  Compare this calculated area with the current
max_area
. If it’s greater, update themax_area
.  Decide which pointer to move. We choose to move the one pointing to the smaller height as moving the other one would not increase the area.
 Calculate the area between the two lines pointed by the
 Continue the loop until the pointers meet. At this point, we would have considered all possible pairs of lines and the
max_area
variable would hold the maximum area that can be formed.
This integrates all our drills into one cohesive solution to the problem.
Q&A
I found a lot of the discussion and proof about this quite opaque, but one thing helped it finally clicked for me (which is sort of proof by contradiction i guess)
You have two heights H_left and H_right, and H_right < H_left, then we know we have two choices, we want to move one of them. If we move the larger one, we cannot increase the height for the simple reason that we are always limited by the shortest, and we would be decreasing ji, the width as well.
To clarify: let’s say we kept the shortest forever, what would happen? Well, ji would decrease, and either we come across a taller block, which doesn’t matter because our shorter one we kept only mattered, or we find a shorter one, in which case that one matters.
Either way we end up with a smaller area, so we must move the shorter one because moving the larger one cannot give an increase in area.
Yes, your understanding is correct and you’ve explained it well. This is the intuitive understanding behind why we move the shorter line inward during the solution. It’s also worth noting that this technique is sometimes referred to as the “twopointer technique” and can be applied to a variety of other problems as well.
In the case of the Container With Most Water problem, moving the shorter line gives us a possibility of finding a longer line, which combined with a somewhat large width (distance between two lines), could potentially give us a larger area. While we decrease the width by moving the pointers, the hope is that we compensate for this reduction by finding a significantly larger height.
On the contrary, if we were to move the longer line, no matter how long the new line is, the height of the container remains limited by the shorter line and hence, it is certain that we cannot get a better solution in this scenario.
In essence, by moving the pointers inward based on the shorter line, we are essentially exploring all possible solutions, with a guarantee that we are not leaving behind any potential better solution. Thus, this technique provides both optimality and efficiency.
we are sure here that moving shorter one is always better than moving bigger one. so we can be greedy here.
Yes, exactly. In this problem, being “greedy” – that is, making the choice that seems the best at each step – leads us to the correct solution.
When we’re deciding which pointer to move, we make the “greedy” choice of moving the shorter one. The logic behind this choice is that by moving the shorter one, we’re giving ourselves a chance to encounter a taller height which could potentially lead to a larger area.
On the other hand, if we move the taller height, we’re guaranteed to not encounter a larger area because the limiting factor (the shorter height) hasn’t changed. So, we can confidently make the “greedy” choice of moving the shorter height, knowing it’s the optimal move.
is it a greedy method? for moving the short one?
Yes, the strategy of always moving the shorter pointer to the next position can be considered a greedy algorithm.
In general, a greedy algorithm makes the locally optimal choice (the best decision at each step) with the hope that these local choices will lead to a global optimum (the best overall solution).
In this problem, the locally optimal choice at each step is to move the pointer at the shorter height, because this gives us a chance to find a taller height that can lead to a larger area. By making this choice at every step, we’re using a greedy approach to find the maximum area.
However, it’s important to note that not all problems can be solved with a greedy approach, as sometimes making locally optimal decisions does not lead to a global optimum. In this problem, we’re fortunate that the greedy approach does lead to the correct solution.
To add further, at any given iteration we discard the shorter bar as we have already accounted for its maximum potential. Future iterations are definitely going to have shorter width, so the only way we have any hope of finding the maxarea in future iterations is to choose the taller bar.
Yes, that’s a great way to further clarify the reasoning behind the greedy approach in this problem.
To illustrate this concept, imagine two bars, one short and one tall, with a wide space between them. The maximum possible area using the shorter bar has already been calculated (since the area is width * height, and the width will only decrease from here on). So, we discard the shorter bar and move to the next one, in the hope of finding a taller bar that might give us a larger area.
This choice, made at each step, embodies the spirit of the greedy approach — making the best decision in the moment, based on the information we have at hand. And as you’ve said, since the width is decreasing with each iteration, the only hope to get a larger area (or even the same area) is by finding a taller height. Hence, we always move the shorter pointer.
This approach ensures we’re not missing any potential larger area when we discard the shorter bar, since we’ve considered its best scenario. It also means that we’re taking the best possible steps to possibly find a larger area. This is the core of the greedy strategy employed in this problem.
I’m curious to find out if there is a version of this problem that starts with i = 0 and j = 1 and move the pointers while right < len(height) to increase chance of getting max_area?
In the problem statement, we’re asked to find the largest area formed by two heights in the array and the distance between them. The area is calculated as min(height[i], height[j]) * (j  i)
. The best strategy to solve this problem is to have i
start at 0 and j
start at len(height)  1
, then move the pointers inward. This approach is advantageous because it starts with the widest possible distance and gradually decreases it.
If you start with i = 0
and j = 1
and increment j
until it reaches len(height)
, the initial distance is the smallest, and the area is likely to be smaller as well. This approach will still allow you to examine all combinations of i
and j
, but it doesn’t align with the greedy strategy of trying to maximize the area by starting with the widest possible distance and looking for taller heights. The time complexity of this approach would still be O(n) because every pair is considered exactly once. However, it could potentially require more iterations to find the maximum area.
Remember, the goal is to maximize the area. Given that area is a product of the distance between the indices and the minimum height represented by those indices, we want to start with the largest possible distance to give us a better chance of finding a maximum area.
Whether you choose to move the higher or lower side, you’re always going to be making the width smaller. The maximum width is the initial state where start = 0 and end = len(height)1. Therefore we are trying to maximize the height. If you choose to move the higher side, you are not maximizing the height because you’re constrained by the lower side. However if you choose to move the lower side, you can potentially find a bar with more height and therefore increase the area.
That’s absolutely correct!
In this problem, you’re trying to maximize the area which is the product of the width (the difference between the indices) and the height (the minimum of the two heights). At the start, you have the maximum possible width. When you move the pointers inward, you’re reducing the width.
If you move the taller pointer, the height for the next area calculation remains the same (as it’s constrained by the shorter bar) and the width decreases  so the maximum possible area would decrease or stay the same.
On the other hand, if you move the shorter pointer, you’re giving yourself a chance to find a taller bar, which could lead to a larger area even though the width is reduced. So to maximize the area, the best strategy is to always move the shorter pointer inward.
This does indeed reflect a kind of greedy algorithm because at each step you make the choice that seems to be the best at that moment (i.e., moving the shorter pointer), with the hope that these local optimal choices will lead to a global optimum solution.
Image Explanation: If we apply force on Left and right side of jelly like substance, it maintain its shape by increasing height. So, in a similar way, to get maximum area we need to find maximum height as we are decreasing width. Common Sense: Suppose, we have rectangle. Area of rectangle(Jelly) = Width * height. => Width = Area of rectangle / Height => Width ∝ 1/Height Since, area of constant. This means, if width decreases, height need to be increased to achieve same area or area greater than previous one. In simple words, We want to increase the area, so this means height of the bars should be increased to compensate the decreasing width to achieve the maimum area. So, we are finding the maximum height from left and right in 2 pointer solution while reducing the width. It’s simple math.
Your analogy and reasoning are absolutely spot on! This problem indeed mirrors the behaviour of a deformable substance like jelly when pressure is applied.
Just as the jelly increases its height to maintain its volume when it is compressed from the sides, in this problem, as we reduce the width (by moving the pointers inwards), we seek to increase the height (by moving the shorter pointer in hope of finding a taller one), in order to maximize the area.
This is exactly the intuition behind the twopointer approach to this problem. We start with the maximum possible width and then attempt to find a taller bar by moving the shorter pointer, in order to make up for the reduced width and hopefully increase the area.
Mathematically, as you’ve pointed out, since Area = Width * Height, if the Width is decreasing, in order to maintain or increase the Area, the Height needs to increase. This is why it makes sense to move the shorter bar  to give ourselves a chance to find a taller one.
I’m not sure what exactly you want to do when “moving two at the same time”, but in general I don’t see how can you miss solutions moving one at a time, to me it seems more likely you’d miss solutions moving two at a time
You’re correct. The twopointer approach to this problem is specifically designed to ensure that we don’t miss any possible solutions.
Moving both pointers simultaneously doesn’t necessarily mean we’ll cover more combinations. In fact, it could potentially cause us to miss some combinations. If we were to move both pointers at the same time, we could miss out on certain combinations that might actually give us the maximum area.
Let’s take an example where we have heights [1,100,6,100]. If we were to move both pointers at the same time, we could miss the configuration where left pointer is at the first 100 and the right pointer is at the second 100, which actually gives the maximum area.
On the other hand, if we move one pointer at a time, specifically the pointer pointing to the shorter height, we ensure that we’re not missing out on any possible combinations. We are effectively trying all combinations where width is ji and height is min(height[i], height[j]) for i < j, thus guaranteeing that we find the maximum possible area.
The reasoning behind moving the shorter one is that by moving the shorter one, there is a chance to increase the limiting height (which is the shorter one), but by moving the taller one, we are only decreasing the width without any chance of increasing the limiting height.
it doesn’t make sense when left height and right height are the same and do a right–. for example: [1,2,100,1,1,1,1,1,2].
The scenario where the heights at the two pointers are the same is an interesting edge case. You’re correct that in such a situation, it doesn’t inherently matter if we increment the left pointer or decrement the right pointer. Either decision would be equally valid.
However, in the algorithm we’ve been discussing, the convention is generally to increment the left pointer. The reason for this is largely to keep the algorithm simple and consistent. It’s easier to say “always move the pointer that is at the lesser height, or the left pointer if the heights are equal” than to specify different behavior for the equalheights case.
In the case of the array you mentioned, [1,2,100,1,1,1,1,1,2], moving the left pointer when we have two equal heights will not cause us to miss the optimal solution. We will still correctly identify that the maximum area is obtained with a width of 2 (from the first ‘2’ to the last ‘2’) and a height of 2, for a total area of 4.
In fact, any approach where we always move one of the pointers, and move the pointer with the lower height when the heights are not equal, will correctly solve this problem. So it doesn’t matter too much whether we decide to always move the left pointer or the right pointer when the heights are equal, as long as we are consistent.
If heights are equal, then the area enclosed is already going to be the max possible for that height so it doesn’t matter which side you move (you can actually move both pointers!).
That’s correct! If the heights of both sides are equal, then the area you can obtain at that specific height is already the maximum because the width (distance between the two pointers) is as large as it can be. You can indeed choose to move either pointer (or both) without risking the chance of missing a larger area.
This is because the area for that height is height*width. Since the height of both sides is the same, the width is the maximum it can be (distance between two pointers). So, if you move either pointer, you’re only going to decrease the width without increasing the height, which can only result in an equal or smaller area.
Therefore, you can confidently move either pointer in this scenario, and it doesn’t matter which one you choose. In the context of the twopointer algorithm, typically we would increment the left pointer by convention, but as mentioned, you could also decrement the right pointer or even move both pointers without affecting the correctness of the solution.
We need to prove that two pointer algorithm will always hit the best solution. The proof goes like: Assume the best container is formed by line x and line y (x < y). Then using the two pointer algorithm (head and tail pointer), we know that eventually one of our pointers will hit x/y sooner than the other pointer. Assume, without loss of generalization, that line y was hit by the tail pointer, then all we need to prove is that this pointer will stay there until line x was hit by the head pointer. To prove this, we only need to show that the length of any line that the head pointer points to currently will always be lower than line y. This is true, because otherwise line x will no longer be in the best solution since we can form a larger container using line y and the line head pointer currently points to.
Exactly! This is a nice clear way to conceptualize the proof of why the twopointer approach works for this problem.
To summarize your explanation in more simpler terms:
We start with two pointers, one at the beginning (head) and one at the end (tail) of the array. We know that one of these pointers (let’s say the tail) will reach one of the lines that forms the maximum area (line y) before the other does. At this point, the tail pointer stays at line y, and the head pointer continues moving.
Now, we need to show that while the head pointer is moving towards line y, it doesn’t encounter a line taller than line y. If it did, that would mean that we could form a larger area with this new line and line y, contradicting our assumption that line x and line y form the maximum area.
So, for the maximum area to be formed by line x and line y, the line at which the head pointer currently points must always be shorter than line y. This justifies our approach of moving the pointer at the shorter line, as we are trying to find a taller line to potentially form a larger area.
This proof ensures that the twopointer approach will always yield the maximum area, supporting the correctness of this strategy.
What if x was reached first? Since y has to move towards x but y is larger than x.
Your question is a good one. Let’s consider this scenario.
If line x (assuming it’s one of the lines contributing to the maximum area) is reached first by the head pointer, the tail pointer (pointing to a line possibly taller than x) will not move until the head pointer finds a line that is taller than the current tail line or until it meets the tail pointer.
Here’s why: our algorithm always moves the pointer at the shorter line. So, if the tail line is taller, it stays in place while the head pointer moves inwards, looking for a taller line.
By the time the head pointer has moved to line y, it has already traversed and discarded all lines between x and y (since they were all shorter than line x and thus couldn’t have given us a larger area).
When both pointers meet at line y, they would have found the maximum area possible, which is formed by lines x and y. This is true because the algorithm has evaluated all possible pairs of lines (by virtue of the two pointers meeting) and the maximum area is recorded during this process.
Hence, even if line x is reached first, our algorithm still works as expected. This demonstrates the correctness of the twopointer approach for this problem.
Another way to think about it, is that for an inner bounding to have a higher volume, there needs to be TWO higher bounds within the subset, therefore we are guaranteed to have to iterate from both sides if there is a correct answer within the current boundary, therefore it doesn’t matter which boundary we iterate from first.
Absolutely! Your intuition aligns with the mechanics of the twopointer approach.
Here’s why this makes sense: For any container, its water holding capacity or area is determined by the minimum of the two boundaries (left and right) and the width between them. Moving the pointer from the shorter boundary might encounter a longer boundary, thus potentially increasing the area.
In contrast, moving the pointer from the taller boundary won’t result in a larger area, because the area is restricted by the shorter boundary and the width is also decreasing.
So we need to move both pointers inward until they meet, as there could be a higher boundary on either side. That’s why the algorithm needs to iterate from both sides.
This process guarantees that we will consider all possible pairs of boundaries and will not miss the combination that yields the maximum area.
does anyone have a Time Limit Exceeded exception (at the 42th test when submitting the code) when running the brute force solution in Python 2?
My tests might not be rigourous, but I tried to run the code on 3 different computers (with a pretty standard configuration) and I didn’t have any problem.
Here is the code:


Yes, your bruteforce solution would indeed cause a “Time Limit Exceeded” error for larger inputs. This is because the time complexity of your solution is O(n^2), where n is the length of the input list “height”. For each element in the list, you are looping over every other element to calculate the area.
LeetCode’s online judge has a time limit for executing solutions, and this limit varies depending on the problem. For most problems, the time limit is between 1 to 2 seconds. Your solution would exceed this limit when the size of the input is large enough, as the time complexity is quadratic.
In contrast, the twopointer approach to this problem has a linear time complexity, O(n). This is because each pointer only moves across the list once, and hence this solution can handle much larger inputs within the time limit.
It’s a good practice to consider time and space complexity when crafting your solutions, especially for larger inputs.
Wasn’t the specification to NOT slant the container? I understood that as saying that the lengths of two lines have to be equal.
The “slant” of the container in the original problem statement is referring to the fact that the container’s sides (the two lines from the array) must be vertical  it is not referring to the heights of the two sides.
In other words, you can’t choose two points and then slant a line between them to form one side of the container. Each side of the container is a line straight up from the xaxis (the base line), and the area of water the container can hold is determined by the shortest of the two lines.
When the problem says “not slant the container”, it means that the container’s sides are always straight up and down, even if they are of different heights. Therefore, the heights of the two lines do not need to be equal.
I hope this clears up any confusion!
What it is trying to say is that you cannot tilt the container to hold more water. If you consider input[0,1] that gives you a triangular container, the amount of water it can hold will be 0 instead of the area of the triangle
Yes, exactly. The “container” in this problem is bound by two vertical lines (elements of the input array) and the distance between them. Even though these two lines might be of different lengths (heights), the container itself is always “upright”, or vertical. That means, the container’s sides are parallel to each other, and they are perpendicular to the base.
The height of the container, for the purposes of calculating the area, is determined by the shorter of the two lines, as the water would overflow at that height. Hence, tilting or slanting isn’t possible as it would not increase the volume of water the container can hold.
In the case of [0,1], the two lines would form a container of height 0 (since the minimum height is 0), and so the area is 0. It doesn’t matter that one side of the container could technically be taller because the container is bound by its shortest side. This illustrates that we’re working with a rectangular area, not a triangular one, hence the statement “not slant the container.”
When you pick a minimum element out of the two and find the maximum area from it. That’s the only maximum possible with that element, so you ditch the chosen element(which is the minimum of the two)). and continue with others.
Since you cannot actually ditch the element you increment the value to the next element from the ditched element (new element = indexOf(ditched_elemen) +1) and now again you go back to step one , pick a minimum element out of the two and bargain with dormamu
Yes, your understanding is correct!
In this problem, our aim is to find two lines which together with the xaxis forms a container that would contain the most water. As you rightly pointed out, at each step, we consider two lines at a time, compute the area they could contain, and compare it with the maximum area found so far.
The decision to ditch the smaller element and move to the next one comes from the fact that, by moving the pointer of the smaller element, we are hoping to find a taller line to compute a larger area (since area = height * width). Because we are reducing the width each time we move a pointer (as the pointers are getting closer to each other), we need to try and increase the height to compensate for the decrease in width. That’s why we choose to move the pointer of the smaller element, because moving the pointer of the larger element can never result in a larger area (as you’ll either end up with a smaller or equal height).
So, to maximize the area, we iterate through the array, at each step moving the pointer of the shorter line towards the other end by one step. We continue this process until the two pointers meet, which ensures that we’ve considered all possible pairs of lines. This way, we ensure that we’ve found the pair of lines that can contain the most water. This is a classic example of the twopointer technique used in many algorithmic problems.
can anyone explain why it wouldn’t result in local maximum?
It won’t result in a local maximum because we’re searching all possible solutions in our approach.
To elaborate, the key to understanding why we don’t get trapped in a local maximum is understanding how the area is calculated: the distance between the lines (j  i) and the height of the shorter line (min(height[i], height[j])).
The reason why we always choose to move the shorter line is that by moving the shorter line, there’s a potential to increase the minimum height, which could lead to a larger area. On the other hand, if we move the taller line, we’re guaranteed not to increase the minimum height (since the remaining line is shorter or equal), and the distance between the lines is decreasing, so the area will either stay the same or decrease.
In this process, we’re examining all pairs of lines, starting with the most distant and trying to increase the minimum height. Since we’re checking all possibilities, we won’t miss the global maximum. So, the algorithm correctly computes the maximum area of water the container can hold.
One quick explanation of why the 2pointer works, is to first look at what we did in the brute force. With brute force, we tried to compare with all “walls”. With 2pointer, we are saying, 1 of the pointers already maximize the other’s “potential”. They are the “greatest partners”. Why? You as a wall reaches your greatest potential when you pair with the farthest wall that is higher than you. That’s why we start from farthest left and right. That satisfies the distance requirment. We always move on from (throw away) the shorter wall of the pair, that ensures one wall is always the 1st wall that is higher than the other.
That’s a good way to look at it! Yes, this problem is essentially about finding two lines (or “walls”) that can contain the maximum amount of water.
In the twopointer technique, we start with the maximum width possible (the leftmost and rightmost walls). Then we progressively reduce the width, moving the pointer of the shorter wall towards the other one.
The reasoning behind this is that any other pair involving the shorter wall will have a smaller width, and thus a smaller area, because the area is calculated as the product of the width (distance between the two walls) and the height of the shorter wall.
In other words, moving the pointer of the shorter wall gives us the potential to find a higher wall that can result in a larger area. This continues until the two pointers meet, ensuring that we have explored all possible pairs of walls.
So as you said, the twopointer strategy ensures that one wall is always the first wall that is higher than the other, which can potentially lead to a larger area, fulfilling its “greatest potential”.
a little speed up suggestion is that when we move the shorter line towards to the other end, these lines shorter than the start line could be ignored until the first longer line to be the new start line.
Yes, that’s a good point. This observation can lead to a further optimization to the twopointer technique we discussed earlier.
Here’s the idea: When we are moving our pointers inward, if we encounter a wall that’s shorter than or equal to the current wall, we can simply skip it. That’s because any water that wall could trap would be less than or equal to what the current wall could trap (since the width is also shrinking), and we’re interested in the maximum area.
In this way, we can potentially skip over many suboptimal walls and speed up our solution. However, please note that this optimization does not change the time complexity, which is still O(n) where n is the number of walls. It simply reduces the number of unnecessary comparisons, thereby improving the efficiency of our algorithm.
This kind of optimization strategy is often called “pruning” in computer science. It helps to eliminate unnecessary computations and make the algorithm run faster.
I would not call this problem a 2pointer problem, but rather a 2d leaner programming problem. You have two dimensions height and width. As in 2d linear programming where you form a convex hull (usually only on one quadrant of Cartesian system) and you traverse from one end to the other end and find the maximum target function. By starting fullwidth, it is as if in a 2d linear programming you start from y axis, let’s say, and you come down along the hull to the x axis.
If you don’t know what linaer programming is, read a brief intro , especially the 2d form, and you’ll see what I am talking about. An example of a 2d linear programming:
maximize the following: z = 2x5y over the following constraints: x>=0 y>=0 x+y<=10 2x5y<=50 etc.
Yes, that’s a very insightful perspective. Drawing parallels between different problem domains can help us uncover effective problemsolving strategies.
In this case, you’re likening the twopointer approach used to solve this problem to a 2D linear programming problem. Just as in 2D linear programming we start from one end of the feasible region and traverse towards the other end to find the optimal solution, in this problem we start with the maximum possible width and traverse towards the center, in search of a higher height that can maximize the area.
This analogy not only provides a different perspective on this problem but also helps us understand the rationale behind the twopointer approach. It explains why we start with the maximum width (since it’s a dimension we have complete control over initially) and why we then decide to move the shorter wall inwards (because we’re trying to find a higher wall, which can potentially maximize the area).
Such insights can be very valuable when trying to develop an intuitive understanding of algorithms and problemsolving strategies.
In two pointer algorithm, after calculating the area, the next step would be decreasing the distance. Now, for getting the max area with the less distance, we definitely in need of two taller blocks which only can make a max area. So, we should change only the small bar in search of tall bar.
Yes, you’ve summed up the logic behind the twopointer algorithm quite succinctly.
At the beginning, we have pointers at the two ends of the list (which provides the maximum possible width). Then we calculate the area of the container using the shorter of the two bars as the height.
In order to potentially get a larger area, we need a larger height since we’re going to be reducing the width on the next step (by moving the pointers closer). Hence, we move the pointer at the shorter bar in hopes of encountering a taller bar.
If we moved the taller bar, we wouldn’t have a chance of increasing the height (since we’re still limited by the existing shorter bar), and so we wouldn’t have a chance of finding a larger area.
This decisionmaking process of always choosing to move the shorter bar is what makes this a ‘greedy’ algorithm, as it always makes the choice that seems best at the current moment in order to increase the area.
I sort the heights, and then for each height I take the first and last index at that height (or higher) and find the area. Then I just loop through the heights keeping the maxarea as I go.
Your approach could potentially work, but it can also be inefficient for certain cases. Sorting the heights first would take O(n log n) time complexity, and then scanning for the first and last index at each height could also be quite timeconsuming, resulting in a less efficient algorithm compared to the twopointer approach.
The twopointer approach works by starting with the widest possible container (with the pointers at the two ends of the array), and then gradually moving the pointers inwards, always choosing to move the pointer at the shorter bar. This approach ensures that we check all possible pairs of bars exactly once, and so it runs in O(n) time, making it more efficient than the sortingbased approach.
However, if you’re looking for a Python implementation for the twopointer approach, here it is:


In this code, max_area
stores the maximum area found so far, and left
and right
are the two pointers. The while
loop continues until the two pointers meet, and in each iteration it calculates the area of the container formed by the bars at the two pointers, and then moves the pointer at the shorter bar inwards. This way, the code checks all possible pairs of bars and finds the maximum area.
Not sure if this counts as proof, but here’s how I understood the solution.
We have two pointers, left which starts at 0, and right which starts at len(height)  1.
We start at the edges because this gives us the greatest chance of finding the maximum area, because we are maximizing one of the parameters of the area, which is width. After calculating this initial area, we move the pointer with the smaller height by 1.
Why not move the pointer with the larger height?
The reason is simply because moving the pointer with the bigger height will not give us any benefit no matter what the the new value is; no matter what we get, it won’t be greater than what we already have. If we get a bigger height, it won’t matter because we are constrained by the height of the smaller pointer. If we get a smaller value, it will definitely be less than the value we previously had.
Therefore, our only choice is to move the pointer with the smaller height because there is a chance we get a bigger value that will increase the total area.
Yes, you’ve captured the essence of why the twopointer approach works for this problem. To make this a bit more formal, we can state it as follows:
Assume without loss of generality that the left bar is shorter than the right bar. The area of the rectangle formed by these two bars is height[left] * (right  left)
. If we move the right pointer one step to the left, the width of the rectangle decreases by 1, and its height is either less than or equal to height[left]
(since height[right  1]
is either less than or equal to height[left]
). Therefore, the area of the new rectangle can’t be greater than the area of the original rectangle.
On the other hand, if we move the left pointer one step to the right, the width of the rectangle still decreases by 1, but its height could potentially increase (if height[left + 1]
is greater than height[left]
), so the area of the new rectangle could potentially be greater than the area of the original rectangle.
Therefore, to maximize the area, we should always move the pointer at the shorter bar. This is the key insight that allows the twopointer approach to work.
I explained it to myself like this Why does this work ?
 you have an established area with the farthest ends of left and right pointers
 Only incentive to move the pointer in either direction is if you can increase the height by enough amount to compensate for the loss of width
 now which one will you move ? of course the one that is smaller.
This solution can be wrong if somehow you move the pointers and miss the optimal pair. Okay now let’s think is it guaranteed that in this approach you will NOT move on from the final solution.
 Let’s assume the answer  two pairs are (L, R)
 Properties about the solution. L and R are closet in heights and farther apart than any other possible solution.
 The only way your solution will miss R is if you encounter l’ >= R and move the right pointer in the left direction
 index[l’] can not be < index[L] because otherwise answer would have l’
 similar logic for the other direction
Yes, your explanation is spot on! Let me rephrase it slightly to emphasize the key points:
We start with the widest possible container, i.e., between the leftmost and rightmost lines. The goal is to find a higher pair of lines that could potentially hold more water, despite the reduction in width.
When we have two pointers L
and R
, we move the pointer that points to the shorter line. This is because by moving the pointer at the taller line, we can’t improve the situation – we’re already limited by the height of the shorter line.
Now, is it possible that we could skip over the optimal pair while doing this? Let’s assume that the optimal pair is (L, R)
. The only way we could skip over this pair is if we encountered another line L'
such that height[L'] >= height[R]
before getting to R
, and moved the right pointer inwards. But in that case, (L', R)
would be a better pair than (L, R)
, which is a contradiction. Therefore, it’s impossible to skip over the optimal pair, and this approach will always find the maximum area.
This logic ensures that our twopointer strategy is both correct and optimal. We’re always moving towards a potential increase in area, and we can be sure that we’re not skipping over any potential better solutions.
What kind of background or thought process is needed to think about the approach which starts by taking area of far ends?
In this problem only two examples are given. Consider the extreme case and create your own example. If it is not provided create your own extreme case. This case should capture the highest area you can possibly calculate. You can modify the first example and imagine the start and end provides the tallest walls. What is the next best possible scenario? You could gradually add more complexity to the example to eventually arrive at example 1 that is given. By that time, you would have gained valuable insights into solving this problem. To some extent this combines working backwards and extreme case analysis.
This problem is a classic example of the TwoPointer technique often used in arraybased problems. However, it also integrates elements of Greedy algorithms. To arrive at the approach of starting by taking the area of the far ends (i.e., the widest container), you might need the following:
Knowledge of TwoPointer technique: This technique is typically used in sorted arrays to search for a pair of elements satisfying certain conditions. In unsorted arrays (like this problem), the twopointer technique can still be applied with a clear rationale.
Understanding of the problem’s constraints: Here, the problem is looking for a maximum area, which depends on both the distance between lines and their heights. As distance is maximized at the far ends, starting there makes sense as it allows you to search for greater heights while gradually decreasing the distance.
Greedy strategy: Greedy algorithms make the locally optimal choice at each stage with the hope that these local choices will lead to a global optimum. In this case, the greedy step is choosing to move the shorter line to seek a possibly taller one.
Analytical thinking and reasoning: You should be able to reason that moving the taller line won’t lead to a larger area, as the area is limited by the shorter line. Therefore, you would only want to move the shorter line.
Experience with similar problems: Practicing other problems involving area calculations, twopointers or greedy algorithms can also help. With enough exposure, you’ll start seeing common patterns and strategies.
Mathematical intuition: This problem relies on an understanding of how area works – it’s a product of height and width. The problemsolving approach requires an understanding that maximizing one factor while looking for opportunities to increase the other could lead to a solution.
All these elements combined can lead one to devise the approach of starting by calculating the area at the far ends, then moving the pointers inward.
These are are great points. One most important thing to consider when analyzing problems like this is to consider the extreme version of the problem. Let’s assume a case where the start and end provide the most water that can be captured. This is the ideal scenario. All other cases will only result in either equal or lesser area.
That’s a very insightful way to approach the problem, and it highlights the importance of understanding the constraints and properties of the problem.
The “extreme version” thought process is beneficial for several reasons:
Simplifies Understanding: Considering the extreme cases often simplifies the problem and provides a clear direction. It helps us see what we should aim for and understand the limits of our solution.
Assists in Identifying Edge Cases: It’s essential to know how your algorithm will behave in the extreme scenarios. It can expose potential weaknesses in the approach that may not be apparent when considering average or typical cases.
Verifies the Correctness of the Solution: If a solution works for the extreme case, it’s more likely to work for the less extreme cases. Hence, if the approach works for the case where the start and end provide the most water, it will likely work for other cases as well.
Promotes Efficient Solutions: If a solution can handle the extreme case efficiently, it will likely be efficient for less extreme cases.
In this specific problem, the extreme case analysis simplifies the problem by providing a “worstcase” scenario for the width (i.e., maximum width). It provides a clear rationale for why we should start from the ends and move inwards, rather than some other approach.
Creating your own examples and working through them is an excellent strategy for solving problems, especially when it comes to algorithmic challenges.
Starting with an extreme case helps you understand the boundaries of the problem and identify the ideal scenario. In this case, the extreme case is when the container’s walls are at the start and end of the array, and they are the tallest.
Gradually adding complexity to the problem helps you understand how different factors impact the solution. For example, adding walls of varying heights between the tallest walls helps you realize that the height of these intermediary walls doesn’t affect the maximum water that can be held.
Working backwards from the solution helps you formulate a strategy to get to that solution. In this problem, you might realize that the maximum water is held when the container’s walls are the farthest apart and the tallest. This insight might lead you to the strategy of starting at the ends of the array and moving inwards, always moving the shorter wall inwards in hope of finding a taller one.
All these techniques combined provide a comprehensive strategy for tackling complex problems, and help improve your problemsolving skills in general.
can someone tell me why we take the minimum of left and right height to find the area?
When computing the area of the water the container can hold, you have to consider the height of the “walls” of the container, which are represented by the lines at the left and right indices in this problem.
The water in the container can’t be higher than the shortest wall because if it were, it would just spill over that wall. Therefore, the height of the container is effectively the height of the shortest wall. This is why we take the minimum of the left and right heights when calculating the area: because that minimum height determines how high the water can be without spilling out.
What is the difference between this problem and https://leetcode.com/problems/trappingrainwater/? . Can somebody help me out please?
Both problems involve water being held by a series of vertical bars, but they are actually quite different:
Container With Most Water: This problem is asking you to find two lines such that they, along with the xaxis, form a container that would hold the greatest amount of water. In other words, you’re trying to maximize the area under the graph, but only between two points. The water can’t be higher than the shortest of the two lines.
Trapping Rain Water: This problem is asking you to find how much water would be trapped if it rained on the terrain formed by the bars. In this case, water can be held between any two bars if there’s a dip in the middle, and you’re trying to calculate the total volume of the trapped water across the entire terrain.
For instance, given the input [3, 1, 2, 3], the “Container With Most Water” problem would have an answer of 6, because the maximum amount of water would be held between the first and last bars. But the “Trapping Rain Water” problem would have an answer of 2, because that’s the total volume of the water that would be trapped between the bars (1 unit of water between the first two bars and another unit between the last two bars).
you start at either end, get the total area there, then depending on which height is higher, you increment or decrement the other and repeat, the other approach you do a lot of repeated work since you’re comparing every single height with every other when you don’t really need to because why would you do calculations where one of the areas is clearly less than the other? you can just avoid that by making that decision with two pointer method which is mainly how you go from O(n^2) to O(n)
What happens if the walls are equal length, and next to one of the walls is a taller wall? Wouldn’t it matter which wall’s pointer you move over?
Ex. we have [1 8 6 2 5 9 8 3 2], if we move the left 8’s pointer over, the area between 8 and 9 will be 8, but if we move the right 8’s pointer over, the max area will be 40. Am I misunderstanding something?
I have the rest of the algorithm figured out and understood, I was just confused on this particular case. I was thinking about doing recursion but recursion isn’t good for runtime.
Your understanding is generally correct, but in this specific scenario, there’s a detail that might have been overlooked.
Consider the array [1, 8, 6, 2, 5, 9, 8, 3, 2]
. When you first consider the two “8"s at indexes 1 and 6 (0based), the area will be 8*(61)=40
. This will be recorded as the max area found so far.
After this, you can move either pointer because they are at the same height. Let’s say we move the left pointer to the right. The next pair of bars will be “6” at index 2 and “8” at index 6. This area will be 6*(62)=24
, which is less than our current max area, so it’s not recorded.
But then you continue moving the left pointer until it’s at index 5 (“9”). Now, your pair of bars are “9” at index 5 and “8” at index 6. This area is 8*(65)=8
which is still less than the current max area.
So, in this example, even though moving the left pointer might seem like a mistake when you encounter the “9”, the algorithm would have already recorded the max area when it first encountered the two “8"s.
This twopointer approach will ensure that you’ve considered every possible pair of bars, so you’re guaranteed to find the maximum area.
That’s why, in the case when both bars are of the same height, moving either pointer will not miss out on the potential larger area.
For the brute force solution, shouldn’t the first loop condition be for left in range(len(height)1): instead of for left in range(len(height)) to avoid index out of range due to left +1 in the second for loop??
Yes, you are correct. If we allow the left pointer to go all the way to the end of the list, then when we add 1 to it in the inner loop, we would indeed get an “index out of range” error. So, the outer loop should indeed stop one short of the end of the list. This adjustment allows the left pointer to go up to the secondtolast element, and then the inner loop allows the right pointer to consider the last element.
The adjusted code would look like:


In this code, ’left’ goes up to the secondtolast index of the ‘height’ list, and ‘right’ is always one step ahead of ’left’, thus ensuring we do not exceed the length of the list.
i realized that greedy algo is not something you’d intentionally apply when solving a problem.. it appears in your head when you are using your “common sense”.
That’s a great insight! Greedy algorithms are often about making the locally optimal choice at each step with the hope that these local choices will lead to a globally optimal solution. In many cases, these “locally optimal choices” can feel like common sense decisions, because they often involve taking the best or most promising choice available at each step.
However, it’s worth noting that not all problems can be solved accurately with a greedy approach. While it may lead to an optimal solution in some cases, in others, a greedy strategy might lead to a suboptimal outcome. That’s why it’s important to understand and prove the correctness of your strategy when you’re designing an algorithm for a problem.
In the context of this problem (Max Area of Water), the greedy strategy of always choosing to move the smaller of the two pointers ends up finding the globally optimal solution, and we can prove this mathematically. So while it might feel like “common sense”, there’s also some deeper underlying logic at work.
How do we know we should use two pointers?
The choice to use the twopointer technique is usually not immediately obvious, but it can often be inferred from the nature of the problem. Here are some indicators that twopointers might be the right approach:
Sequential Data Structure: Twopointer techniques are typically used on linear data structures like arrays and linked lists, which have a clear notion of “beginning” and “end”.
Ordered Data or Requirement of Comparisons: Often, twopointer problems involve sorted arrays or require comparing elements.
You need to track or compare two positions: In this case, we want to compare all pairs of positions to maximize the area of water. The twopointer approach is a way of systematically visiting all pairs without repetition.
Sliding window problems: If the problem is asking for something about subarray (continuous subsequence) like maximum sum of subarray of size ‘k’, smallest subarray with sum greater than a given value, longest substring without repeating characters, etc. These problems can usually be solved using twopointer technique where one pointer represents the start of the window and the other represents the end.
Aim to reduce Time Complexity: If a naive solution is available with higher time complexity (say, O(n²)) and you are looking to optimize it, then twopointer technique often helps to bring down the complexity to linear time (O(n)).
In the context of the “Max Area of Water” problem, you are trying to find two lines which, together with the xaxis, forms a container that would hold the greatest amount of water. The problem’s phrasing doesn’t directly suggest a twopointer approach, but by observing that you are working with a linear data structure (an array), and you need to compare pairs of elements (the heights of the lines), you can identify that two pointers might be a good strategy.
Can someone explain how is this a greedy problem? Isn’t this just a DP approach using 2 pointers?
This problem can indeed be considered a greedy algorithm, but it doesn’t necessarily fit the mold of traditional dynamic programming (DP). Let’s break it down:
Greedy Algorithm: A greedy algorithm is one that makes the “best” choice at each decision point with the hope that these local optimums will lead to a global optimum. In this problem, at each step, the algorithm greedily decides to eliminate the smaller of the two lines, because that’s the one that’s limiting the current area and has no chance of contributing to a larger area in the future. By always choosing to eliminate the smaller line, the algorithm is making a greedy decision.
Dynamic Programming (DP): DP is a method for solving problems by breaking them down into smaller subproblems and using the solutions of those subproblems to build up the solution to the larger problem. It often involves storing intermediate results in a table and using an iterative or recursive formula to fill in that table. In this problem, we don’t really have subproblems that build upon each other, nor do we have a need to store intermediate results for later use. So, it doesn’t quite fit the traditional understanding of DP.
Using two pointers is a separate technique that is often used in conjunction with other strategies (like greedy or DP), and it’s utilized here to efficiently traverse the array from both ends.
In summary, this problem is more accurately described as employing a greedy strategy using two pointers, rather than being a DP problem.
Another explanation for the twopointer approach:
Assign a left pointer to the far left wall, and a right pointer to the far right wall. There is no greater distance than the distance between these two walls. This forms our first container. Note that the area of any container is bounded by the height of the shorter wall because no matter how tall either of the walls are, water will only fill up to the height of the shorter wall. For example, if the far left wall has a height of 1 and the far right wall has a height of 8, and the distance between them is 8, then the area of the container is 1 * 8 = 8. If the far right container had a height of 12, the area would still be 8.
To make progress, we’ve got to move either the left or right pointer inwards. Which pointer do we move?
Notice that if we move the pointer pointing to the taller wall inwards and keep the pointer to the shorter wall unchanged, the distance between the pointers shrinks, so naturally the area shrinks as well. We know that our area is bounded by the wall with the shortest height, so no matter how tall the right pointers’ wall is, the distance will continue to shrink (especially if the right pointer now lands on a shorter wall than the left one, which leads us to an even smaller container). We need to compensate for the shrinking distance by maximizing the height of the shorter wall, which could potentially yield a container with a larger area. So, we should instead move the pointer pointing to the shorter wall inwards, which gives us an opportunity to form a deeper container and one that could hold more water. This is what makes it a greedy problem: choose (and save) the best option available at each step and don’t worry if the current result will bring the overall best result.
What if the walls are the same height? Which pointer do we move?
Consider these scenarios: If we choose to move the left pointer inwards, and the height of the next wall we are about to move to is greater than the height of the wall at the current right pointer, then we would end up also moving the right pointer inwards since the right wall would then be shorter.
If we instead choose to move the right pointer inwards, and the height of the next wall we are about to move to is greater than the height of the wall at the left pointer, we would end up moving the left pointer inwards since the left wall would then be shorter.
No matter which pointer we move, if there exists a future wall that is taller than the current walls, the pointer we didn’t choose to move initially will eventually have to move (!!).
So, which pointer do we move?
The answer is either or both. Moving both could be two times faster than moving one of them (ie. if the array has N elements that are all the same, you will run N / 2 loops by moving both pointers, and N by moving one pointer. The time complexity is still O(N) though.
Study these examples to drive the point home: [2,2,2,10,2,2,2], [2,1,10,10,2,1,1].
You’ve made a very clear and detailed explanation for the twopointer approach used in solving this problem. Let me summarize it:
We start with a container formed by the two farthest walls (left and right pointers), as it gives the maximum distance between walls. The area of the container is limited by the height of the shorter wall.
To improve the container area, we need to move either the left or right pointer inwards. If we move the pointer at the taller wall, the area will shrink as the distance between walls reduces. So, to potentially increase the area, we move the pointer at the shorter wall.
In case the walls have the same height, we can move either pointer. If a future wall is taller than the current walls, the pointer at the current wall will have to move in either case. Moving both pointers when they’re at walls of equal height can be faster, but doesn’t affect the overall time complexity of O(N).
The decisionmaking at each step to achieve an optimal outcome is what makes this problem a greedy problem.
Is the two pointer approach considered the same as “sliding window”?
While both the “two pointer” and “sliding window” techniques involve moving pointers through an array or list, they are not exactly the same and are typically used to solve different kinds of problems.
The twopointer approach often involves having two pointers that start at different positions (like the two ends of an array) and move towards each other, as is done in this problem. The pointers may also move in the same direction, with the condition that one pointer is always ahead of the other. This method is often used when we’re searching for a pair of elements that satisfy a certain property, like in this problem, where we’re looking for the pair of lines that can trap the maximum amount of water.
On the other hand, a sliding window approach typically involves maintaining a “window” of elements in an array or list. This window “slides” over the array, changing its size dynamically based on the problem constraints. This method is often used when we’re looking for a contiguous subarray that satisfies a certain property, like in problems where we’re asked to find the longest contiguous subarray with a certain sum.
In this particular problem, we’re using a twopointer approach rather than a sliding window approach. The two pointers, starting from the ends of the array, move towards each other, trying to find the maximum area between two lines. The movement of pointers doesn’t maintain a consistent window of elements, hence it’s not a sliding window approach.
Yes, you could consider this problem as an example of a “Kissing Pointers” approach. The term “Kissing Pointers” describes the method used in this problem quite well.
In this approach, we start with two pointers at opposite ends of the array (the leftmost and rightmost elements). The pointers then “move towards each other”, or “kiss”, until they meet in the middle. Along the way, we use the positions of these two pointers to perform our necessary calculations (in this case, calculating the maximum area of water that can be held between the two lines pointed by these pointers).
This approach can be particularly efficient for certain types of problems, as it allows us to examine all pairs of elements in the array in a single pass. In the context of this problem, it helps us efficiently identify the pair of lines that can trap the maximum amount of water.
In Move Zeroes problem we have two pointers but they are Reader Writer Pointers
Yes, that’s correct. In the Move Zeroes problem, we use what’s often referred to as “readwrite” pointers or “slowfast” pointers.
The general idea in this strategy is that one pointer (the “fast” or “read” pointer) goes through the array to read the elements, and the other pointer (the “slow” or “write” pointer) is used to write the necessary values.
In the context of the Move Zeroes problem, the “read” pointer is used to iterate through the array to find nonzero elements, and the “write” pointer is used to write these nonzero elements at the appropriate position in the array. This strategy allows us to move all the zeros to the end of the array in a single pass, resulting in a very efficient solution.
It’s important to note that although both the Move Zeroes problem and the Container With Most Water problem use two pointers, the way the pointers are used is quite different in each problem, reflecting the different problem requirements and constraints.
Can someone explain me the code block for if (height[left] <= height[right]) { left++; } else { right–; }
Is it just random condition check to increase and decrease the left and right respectively?
The code block you’re referring to isn’t random; it’s a crucial part of the algorithm. This conditional block determines which of the two pointers (left or right) should be moved. The goal is to maximize the area that can be contained between the two pointers (considering them as two vertical walls).
The area is calculated as the product of the distance between the two pointers and the height of the shorter wall. So, in order to potentially increase the area, we should try to increase the height of the shorter wall.
In this block of code:


The pointer with the shorter wall height (or left pointer if the heights are equal) is moved one step. If the height at the left
pointer is less than or equal to the height at the right
pointer, then the left
pointer is incremented. Otherwise, the right
pointer is decremented.
This is based on the idea that moving the pointer with the taller height inwards won’t increase the area, because the height of the shorter wall is what determines the maximum height of the water the container can hold. So, it makes sense to move the pointer with the shorter height in the hope of encountering a taller wall.
Two pointer approach from ends with following rule: move the shorter one as moving the longer one cannot possibly result in a greater area. If both heights are equal move the smaller one, because why not.
The equal case could be improved here: if height[left] = height[right], we can update both left and right! This is because there won’t be a larger area with the current height as the limiting edge!
BTW, I find this idea useful to understand the problem: you can think about each calculated area is the maxArea of the smaller height. Say your smaller height is height[left], then you can never find a larger area with height = height[left]. This is exactly why the algorithm would have a smaller runtime complexity: because instead of having an area of all the combos of bounds, we don’t need to use the smaller bound to couple with other inner bounds! This is very similar to the Q167 version of two pointers for twoSum.
Absolutely, your suggestion on updating both left and right when their heights are equal is a valid optimization. This is because no larger area can be obtained with the current equal heights, so it’s efficient to move both pointers inward.
Your insight about the problem is quite valuable. When we calculate an area using a specific height, that is the maximum area that can be achieved with that specific height, because the distance (width) is the maximum possible at that moment. This eliminates the need to test the current limiting height with other internal heights, thus reducing the runtime complexity.
The algorithm is indeed reminiscent of the twopointer approach in the twosum problem (Q167), where we move the pointers based on the sum of the numbers at the pointers’ positions relative to the target value, which simplifies the problem from checking all possible pairs to a linear scan.
Brute force is doing n^2 calculations. The key to solving this problem is asking yourself : Can I skip some calculations in favor of the others ? you can, just leave the smaller of every pair you compare from further comparisons
Yes, that’s a correct observation. The key idea of the twopointer approach (which is used to optimize the brute force approach) is to understand that you don’t need to calculate the area for every possible pair of walls. Instead, you can skip some calculations by eliminating pairs that can’t possibly lead to a larger area.
The logic is simple: you start with the widest possible pair of walls and calculate the area. Then you move the pointer at the shorter wall towards the other one. This is because the area is limited by the height of the shorter wall and you hope to find a taller wall by moving the pointer. At the same time, you are reducing the width (the distance between the walls), but you hope the potential increase in height will compensate for this reduction. By doing this, you can avoid calculating the area for many pairs, which reduces the time complexity from O(n^2) to O(n).


Can someone explain why this is wrong?
The reason your code may not work as expected is because of the way you are deciding whether to move the left or right pointer.
In your code, you’re calculating the area of both possible moves (moving left pointer to right, or moving right pointer to left), and you choose the move that gives a larger area. But this is a kind of ‘greedy’ decision that doesn’t take into account the future possibilities.
Remember that the water container’s capacity is determined by the shorter wall. In your method, it’s possible that you move the pointer from a shorter wall to an even shorter one just because it temporarily gives a larger area. But this could mean missing the chance of pairing the original shorter wall with a much taller wall later on, which could have given a larger area overall.
The commonly accepted solution for this problem decides which pointer to move based on the height of the wall at the current pointers, not based on the temporary area. We always move the pointer from the shorter wall because we want to find a taller wall to pair with the current taller wall. This guarantees that we don’t miss any possible larger area. Here is the commonly accepted solution for comparison:


In this code, the ‘greedy’ decision is not based on the temporary area, but on the hope of finding a taller wall to pair with the current taller wall. This makes sure we don’t miss any potential larger areas.
The algorithm works based on the minimum unit is 1. If the float number is allowed this won’t work. Intuitively, you can think it as a problem trying to found an area close to square. Area = L*H, in the beginning, L is given as the maximum value = len(height), H is the element in the height. Every time, L get 1 unit short and find the good H, record all the results and get the maximum.
That’s a very astute observation! The algorithm indeed works because the units are integers and the width of the container decreases by 1 with each step. The goal is to find an optimal balance between the height and width to maximize the area. If floating numbers were involved, the problem would be significantly more complex because you wouldn’t be able to guarantee that you’re not missing a better solution when you’re moving the pointers.
As you said, the problem can be intuitively understood as an attempt to find an area close to a square. When the width is maximized (i.e., at the beginning of the process), we try to find the highest possible height. As we step inwards, reducing the width, we’re hoping to find a height that can compensate for this loss in width. This results in the area being maintained or even increased. The algorithm stops when the pointers meet, at which point we can be sure that we have found the maximum possible area.
Remember, this approach works specifically because the units are integers and the change is by whole numbers. If we had fractional values, this algorithm might not yield the correct result.
Assume that we have two heights H[i] and H[j] and H[i] < H[j] and i < j. Then the largest area which right line is left to j and left line is i is just (ji+1)*(H[i]).(Because H[i] limit the height) So we can eliminate line i (don’t use it as the left line anymore） In fact, we don’t need to take care of other right lines right to j when the left line is i. Because when we are considering j, we have already considered the max area which right side is from j+1 to n1, these right lines are all eliminated in the same way. Then since we won’t use line i again, we can let i plus 1 and keep doing this. So I think this is a precedure that keep considering of possible lines and eliminating lines.
Yes, that’s a great explanation of why this twopointer approach works in this problem. To rephrase for better understanding:
Imagine we have two walls H[i] and H[j] (i<j) and the height of H[i] is less than the height of H[j]. The largest area that can be formed using the wall at i as the left boundary and any wall to the left of j as the right boundary is (ji+1)*H[i]. This is because the height of H[i] limits the height of the water we can store.
This means that we can discard H[i] as a left boundary for future areas. Why? Because any area that would use H[i] as the left boundary and a wall to the right of j as the right boundary, can’t be larger than the one we’ve just considered (the one formed by H[i] and H[j]) because H[i] would still limit the height and the distance between the walls would be less than or equal to (ji+1).
Following the same logic, we can see that, for each H[i], we can safely ignore all the right boundaries that are further to the right than the currently considered right boundary j. This is because when we considered the wall at position j as a possible right boundary, we have already considered all the potential areas formed by H[i] as the left boundary and any wall from j+1 to n1 as the right boundary.
So at each step, we can safely discard the wall with the lower height, and move on to the next potential wall. This way, we keep reducing the search space, hence making this approach very efficient.
How do you know which possible walls will not contribute to a higher area to eliminate them?
In this problem, the twopointer approach helps us decide which walls will not contribute to a larger area and thus can be eliminated.
Here’s how:
We start with two pointers, one at the far left (i.e., the first wall) and one at the far right (i.e., the last wall). At each step, we calculate the area of the water that can be trapped between these two walls. The area is given by the minimum height of the two walls times the distance between them.
Now, to progress, we need to move one of the pointers. If we move the pointer that’s at the taller wall, the area is definitely going to decrease. This is because the distance between the walls will decrease and the height will be limited by the shorter wall (which remains the same). Therefore, moving the pointer at the taller wall can’t lead to a larger area.
Hence, we decide to move the pointer that’s at the shorter wall. This gives us a chance to possibly encounter a taller wall that might, combined with the current taller wall, give us a larger area.
So, at each step, we’re eliminating the wall that’s shorter of the two currently considered walls, because keeping it and trying to pair it with other walls will not give us a larger area. We keep repeating this process until the two pointers meet.
To summarize, in this problem, the walls that get eliminated are those that are shorter than the other wall they’re paired with to form the watertrapping area. And we can confidently eliminate them because keeping them and trying to pair them with other walls can’t lead to a larger area.
Why it’s not a Dynamic Programming case? Is it because it doesn’t have a repeating subproblem?
Exactly, you hit the nail on the head. The essence of Dynamic Programming (DP) lies in solving complex problems by breaking them down into simpler, repeating subproblems, and then using the solutions to these subproblems to construct solutions to larger problems. This technique is often applied when a problem shows the characteristics of overlapping subproblems and optimal substructure.
In the case of the “Container with Most Water” problem, it doesn’t quite fit the DP paradigm. Although you might argue that the problem has an optimal substructure (i.e., finding the container with the most water involves finding the maximum areas between all pairs of bars), it does not have overlapping subproblems.
The use of the twopointer technique in the solution doesn’t involve building a solution from previously calculated subproblems, but rather systematically narrowing down the search space based on some greedy choice (moving the pointer pointing at the shorter wall). Thus, it’s more apt to consider this problem as an example of the twopointer or greedy strategy rather than a DP problem.
(1) Place left pointer in the beginning and right pointer in the end. (2) current water = (right  left) * Math.min(height[left], height[right]) basically, how much water we can obtain is by the area of rectangle. right  left is wide and Math.min(height[left], height[right]) is height. (3) Then we try all the combination by narrowing down the range of two pointers.
Yes, you’ve got it right! Your understanding of the problem and the algorithm to solve it is spoton.
The twopointer technique works very well in this case. Here’s a simplified rundown:
Initially, the widest possible area (i.e., the one between the first and the last bar) is considered. So, the left pointer is at the start of the array, and the right pointer is at the end.
The potential amount of water (i.e., area) that can be trapped is calculated by finding the minimum height between the two bars pointed at by the left and right pointers, and then multiplying it by the width (which is the distance between the two pointers).
To try and find a larger area, the pointers need to be moved. The decision on which pointer to move is made by observing the heights at the current pointers: the pointer with the smaller height is moved. The rationale behind this is that by moving the pointer with the larger height, we won’t be able to increase the limiting height (which is determined by the smaller of the two), and the width between the pointers (i.e., the base of the rectangle) is definitely decreasing, so the area can’t increase.
This process of calculating the area and adjusting the pointers is repeated until the two pointers meet, ensuring that all possible areas are considered. The largest of these areas is then the solution to the problem.
Yes but how did you come to the approach of setting left to first column and right to last? Is there a way to solve by initializing both to first column and moving towards the end?
The approach to initialize the left pointer at the first column and the right pointer at the last column is based on the idea that we’re trying to maximize the area of the container, which is determined by both the distance between the columns (i.e., the width of the container) and the height of the shorter column.
By starting with the widest possible container (i.e., left at the first column and right at the last column), we are giving ourselves the best possible start in terms of width. From here, we can try to improve the height of the shorter column, which could potentially increase the area.
Starting with both pointers at the first column and moving towards the end would not be as efficient. The reason is that if we start with both pointers at the beginning, the width of our container is initially 0, so the area is also 0 regardless of the height. It’s only when we start moving one of the pointers that we can start forming a container and capturing some water. Moreover, it would not give us a systematic way to explore all possible pairs of columns as efficiently as the twopointer approach. In other words, the strategy of which pair to explore next would not be as clear if we started with both pointers at the first column.
In contrast, by starting with the widest possible container and always moving the pointer at the shorter column, the twopointer approach provides a clear and efficient strategy to explore all possible pairs of columns: Start with the widest possible container, and at each step, try to increase the height of the shorter column by moving its pointer. This will ensure that all possible containers are considered and the maximum area is found.
Since we have two variables that we can control to maximize the area, we first greedily choose the widest possible width than we gradually check if there is a way to find a higher height in order to increase the area even further.
Exactly! The objective is to maximize the area, which is the product of the width and the minimum height. Since the width is determined by the distance between the two pointers and the height is the minimum height of the two points, we initially set our pointers to the widest possible distance (i.e., at the two ends). Then, we gradually move the pointer at the shorter wall towards the other pointer in search for a taller wall.
This approach ensures that we are examining the widest containers first, and as we gradually decrease the width by moving the pointers, we have the potential to find a container with a taller height. This dual approach of decreasing width and potentially increasing height allows us to fully explore the possible areas and find the maximum.
The following facts summarize my understanding of the proof for the O(n) solution: (1)You have an n^2 search space you need to search for the solution. (2)Let’s start enumerating (and pruning) this n^2 search space in search of the solution, starting from the pair (0, n1). And we will move inwards. (2)For any pair (i, j) s.t. h[i] < h[j], it is not possible to find a k (i < k < j) such that (i,k) is a better solution than (i,j). Why? Because either h[k] > h[j] or h[k] < h[j]. In either case, the area enclosed by (i,k) will necessarily be less than the area enclosed by (i,j). (3)The above fact immediately implies that the search space is immediately pruned for future solutions that necessarily have “i” as one pipe, once (i,j) above is visited. (4)So, we can immediately discard all future candidates including “i”, and only focus on exploring the rest of the remaining search space, by exploring (i+1,j) onwards. (5)Therefore, on each step, you move either the left or the right pole while exploring the search space, without losing any potential solution in the pruning process. You get the solution in one pass.
Yes, you have summarized the key points of the twopointer approach quite accurately. Here’s a concise summary of your understanding:
 The total search space for the solution is quadratic, i.e., n^2.
 We start by examining the widest container, i.e., the one formed by the farthest two lines.
 If the height of the line at position i is less than the height of the line at position j, there’s no possible line k (where i < k < j) that could form a better solution with line i than the one with line i and line j. This is because the area is limited by the height of the shorter line.
 Having established this, we can exclude any future solutions that include line i from our search space, thus reducing the problem size.
 Consequently, we proceed to the next step by moving the pointer at the shorter line inwards.
 This approach guarantees that we explore all possible solutions in a single pass, thus giving us an O(n) solution.
This process effectively prunes the search space, allowing us to avoid unnecessary computations and therefore optimize the solution.
Given a container with walls a, b such that a <= b, the wall a cannot be a wall in a new container larger than the current one because any future container will be narrower, so at every iteration we can reduce the search space by excluding the wall a.
Yes, that’s correct. This is the fundamental logic behind the twopointer approach to solving this problem.
In each iteration, we compute the area formed by the current pair of walls. Since the height of the water is limited by the shorter wall, and the area is calculated by the product of this height and the distance between the two walls, we know that a taller wall could potentially lead to a larger area because future containers will be narrower. Hence, we can safely exclude the shorter wall (in this case, wall ‘a’) in our search for a larger area, effectively reducing the search space.
So we shift the pointer pointing to the shorter wall inward, hoping to find a taller wall and thus possibly increase the area. This process continues until the two pointers meet, ensuring all potential pairs of walls have been considered.
When following the 2 pointer approach, in cases where both the heights are same, how can we be sure that the pointer which we are moving (say left pointer) will give the better result. Can someone please help me understand how this method is exhausting all possible solutions.
In the twopointer approach, if the heights are the same, it does not matter whether you move the left pointer or the right pointer because the height limiting the water level is the same on both sides.
The reason we are certain that we’re not missing a better solution is that by moving either pointer, we’re exploring a new potential pair of walls. Since we started with the widest possible container (the distance between the first and last positions), the only way to find a larger area is to find a taller wall.
Remember that, in this problem, the height of the shorter wall and the distance between the two walls determine the maximum amount of water a container can hold. When the two walls are of the same height, moving either pointer can potentially find a taller wall.
By moving either the left or right pointer (or both), we are effectively exploring all combinations of wall pairs, thus exhausting all possible solutions. This ensures we can find the pair of walls that can contain the most water.
can this be done by using dynamic programming. I see that it meets the conditions to be a dp problem.
maximum of something is asked. current state depends on previous state.
While the problem might seem like a good candidate for dynamic programming (DP) at first glance, it actually isn’t. Here’s why:
Overlapping Subproblems: While it might seem like we have overlapping subproblems, because we’re trying to find the maximum area at each step, the optimal substructure property doesn’t hold here. In dynamic programming problems, the solution to a problem depends on solutions to smaller instances of the same problem (such subproblems). However, in this case, the solution of finding the maximum area between two points doesn’t necessarily help solve the larger problem, because the global maximum area isn’t constructed from the maximum areas of subproblems.
Optimal Substructure: Dynamic programming problems typically exhibit the property of optimal substructure, which means the optimal solution to a problem can be constructed from optimal solutions of its subproblems. However, in this case, the solution isn’t built from the solutions of subproblems. For example, knowing the maximum area you can obtain by only considering the first half of the bars doesn’t help you find the maximum area considering all bars.
Therefore, the twopointer or “shrink from both ends” approach is more suitable and efficient for this problem. This approach doesn’t require us to solve multiple overlapping subproblems like a typical DP approach would. Instead, it exploits the property that the area is limited by the shorter of two bars and hence reduces the problem space by moving the shorter bar.
Somebody please help: I am getting “Time Limit exceeds” error with below code, can somebody please help me what i have to correct to avoid Time Limit exceeds error,


The reason you’re getting a “Time Limit Exceeded” error is that your code is using a brute force approach to solving the problem. Specifically, you’re using two nested loops to iterate through all possible combinations of the “height” array, which results in a time complexity of O(n^2). For larger inputs, this can lead to significant computation time and eventually exceed the time limit set by the online judge.
You can avoid this issue by using a more efficient algorithm, such as the twopointer approach, which has a time complexity of O(n). Here’s how you might implement this in Java:


In this code, we initialize two pointers at the two ends of the array. At each step, we calculate the area, update the maxArea if the current area is larger, and then move the pointer at the shorter side inward until the two pointers meet. This way, we ensure that we check all possible pairs of walls but only do a single pass through the array, resulting in a linear runtime.
I am not able to understand the answer of this [1,8,6,2,5,4,8,3,7] example if we choose row (index based on 0 ) row a1(8) to a8(7). this container can hold water row a1(8) to a6(8) equal to 85 + a6(8) to a8(7) equal to 72. total of this will be 40+14=54. please correct me what I am missing in this
Your approach seems to be dividing the problem into multiple containers. However, the problem statement says:
“You are given an array of positive integers where each integer represents the height of a vertical line on a chart. Find two lines which together with the xaxis forms a container that would hold the greatest amount of water.”
This means we are looking for the greatest amount of water that can be held between any two lines (considered as walls of a container). The water doesn’t get divided into multiple containers.
So, in the case of [1,8,6,2,5,4,8,3,7]
, the largest container is between the lines of height 8 at index 1 and 8 at index 6, resulting in an area of (61)*min(8,8) = 5*8 = 40
, as distance between the lines is 61=5
and the shorter of the two heights is 8
. The two walls of the container are inclusive, which is why we subtract 1 from the difference of the indices.
We don’t add up areas from different possible containers. We only need the maximum possible area from a single container.
My main question after applying the 2pointer approach is why does it work? Here’s my understanding:
Our goal is to find the pair of 2 longest pillars that are farthest away from each other. As the equation to compute capacity is breadth*length which is here: (Rightpillar_indexLeftpillar_index) * minimum( rightpillar, leftpillar) Whenever a pointer moves, there is a reduction of breadth by one unit. This won’t reduce our capacity if we find a sufficiently long pillar (either right or left pillar) that can still give us a max capacity value. In the equation: (Rightpillar_indexLeftpillar_index) * minimum( rightpillar, leftpillar), we need to maximize > minimum( rightpillar, leftpillar) in the hope that when multiplied by the breadth will still result in a high number. The entire 2pointer approach here helps to maximize this term.
Yes, you’ve captured the essence of the twopointer approach to this problem!
The twopointer approach is indeed trying to maximize the area, which is the product of the distance between the two points and the minimum of their heights.
To start, we have the widest possible container (the distance between the first and the last lines), which also includes the possibility of having the highest lines as the boundaries of the container. The area of this container will be the distance between the lines (the width of the container) times the height of the shorter line (since the water would overflow the shorter line).
Then we start moving the pointers inward. If we moved the pointer at the taller line, the height of our container would stay the same or decrease, and the width of our container would definitely decrease, so the maximum possible area would decrease.
But if we move the pointer at the shorter line, the height of our container could possibly increase, and although the width of our container will decrease, we might still get a larger area. So at each step, we move the pointer at the shorter line in the hope of finding a taller line that can increase the container’s area.
The reason this works is that by always moving the pointer at the shorter line, we are exploring all possible pairs of lines. We never move the pointer at the taller line, so for every pair of lines, at least one of their heights has been the shorter line in a previous pair of lines. This ensures that we have looked at all possible pairs of lines, which ensures that we have found the pair of lines that can contain the most water.
I thoroughly enjoyed  and spent far too much time  on this one! Thank you! In my case, while I had the initial instincts of the twopointer approach, for whatever reason I couldn’t come up with a straightforward algorithm of how to decide which side to move in, so I instead coded up the brute force and optimized from there. Through multiple iterations, I got more and more insight into how I could skip over some comparisons, knowing they couldn’t be better, and that eventually led me back to the twopointer solution with much deeper understanding. I spent a lot of time focusing on how to “skip over” area calculations that I knew would be less, so with tracking a few additional datapoints I was able to loop more quickly without doing the area calculation. Yes, doing the area calculation (a min(), subtraction, and multiplication) isn’t expensive, but it was fun to go that extra depth. It could lead to a nice followup in an interview if you have extra time  something like “let’s assume, just for fun, that doing the area calculation is really expensive, so how can you optimize to not do the calculation when you’ve already seen enough to know it won’t be better”. The insight is that you can keep moving in from the shorter edge until you find an edge that is taller.
That’s a fantastic approach to problem solving! The methodical way you broke down the problem, starting with a brute force approach and then gradually optimizing and gaining deeper insights, is very impressive.
Even though the twopointer approach is known for this problem, going through the process of discovering it on your own is a great exercise and probably gave you a deeper understanding of the algorithm than if you’d simply read about it.
Your idea about assuming that the area calculation is expensive and trying to optimize it away is interesting. It’s this kind of deeper thinking that makes for great problem solving skills, and it can often lead to unique and efficient solutions that might not be immediately obvious.
In an interview setting, explaining this kind of thought process to an interviewer would likely be well received. It shows that you’re not just mechanically applying known algorithms, but really thinking deeply about the problem and how to optimize your solution.
Well done on your problem solving process, and keep up the great work!
What are the series of steps in logic that you need to take to get to the point that you should realize that starting the pointers at the extreme ends of the arrays is the way to go?
Arriving at the conclusion that you should start pointers at the extreme ends of the array for the ‘Container With Most Water’ problem requires understanding a few key insights:
Area: The amount of water the container can hold is determined by the shortest wall and the distance between the walls, or formally,
min(height[i], height[j]) * (ji)
. This implies two variables control the water that the container can hold: height and width. To maximize the area, you will need to maximize both.Starting Point: If you start at one end (say, the leftmost wall) and just move rightwards, you can increase the height, but you decrease the width simultaneously. This gives no guarantee of a larger area. This problem doesn’t seem straightforward if we start from one end and go to the other, which leads us to think about using two pointers.
Two Pointers: A twopointer strategy is useful when you need to compare two elements and there is a way to eliminate one of them in each step. Here, we have two walls of the container, and after calculating the area, we can eliminate one of them.
Choice of Pointers’ Movements: When the pointers are at the ends, the width is at its maximum. As we move pointers towards each other, width decreases. Therefore, to maintain a chance of finding a larger area, we need to increase the height. That’s why we move the pointer pointing to the shorter wall, as there is a chance to find a higher wall.
So, putting it together, you can understand that starting pointers at the extreme ends of the array gives us the maximum width initially, and a systematic way of possibly increasing the height, which may lead us to find the container that can hold the most water.
The question is confusing with the “water” concept. Instead, the question should basically ask the maximum the area of rectangle between two lines. In a water based scenario, the bars in between any two chosen lines matter as water would settle on top of the bars.
While the problem is indeed framed in the context of water, the key point to understand is that the problem is actually asking for the maximum area that can be formed between the vertical lines using the shorter line’s height as the height and the distance between the lines as the width of the rectangle. The bars in between don’t actually affect the maximum area that can be formed between any two bars, because the water (or the area) is limited by the shorter of the two bars.
It’s a somewhat counterintuitive concept at first, but if you think about it, the water contained between two bars is indeed determined only by the height of the shorter bar and the distance between them, not by any bars that may be in between. This is because any bars that are shorter than the two outer bars won’t contribute to the overall capacity (the water will just flow over them), and any bars that are taller than the two outer bars won’t make any difference either (since the water level is limited by the height of the shorter outer bar).
You could indeed think of it as the maximum area of a rectangle between two lines if that’s easier to conceptualize. The important thing is to understand the principle that’s at play here.
I had the twopointer approach right off the bat, but was complicating the condition too much.
I was trying to just use rules / check conditions about the change in height of the bar vs the amount the base was contracting, vs which side was taller, etc… it was getting really complicated and was error prone for edge cases. I only began down that path because I wanted to avoid calculating the area on each pass (but that’s stupid). Simplifying it to just calculate the area directly on each pass made it much easier.
That’s a great realization and a perfect example of why it’s often a good idea to start with the simplest possible solution and only complicate it if necessary. Sometimes, trying to optimize or overthink a problem right out of the gate can actually make it harder to solve and introduce unnecessary complexity.
In this problem, your first instinct of using a twopointer approach was spoton. It’s understandable to want to avoid calculations in each iteration, but as you realized, the overhead of checking all those conditions and managing edge cases can actually end up being more complicated and errorprone than just calculating the area at each step.
The key insight here is that by always moving the pointer at the shorter side, we’re ensuring that we’re exploring all potential containers that could possibly have a larger area. And since calculating the area is a very cheap operation (just a multiplication and subtraction), it doesn’t significantly affect the performance of our solution.
So yes, sometimes, the straightforward approach is the best one. In fact, many problems can be solved with simple techniques like this, so it’s always a good idea to try the simplest solution first, and only consider more complex solutions if necessary.
Hows this problem different than the Trapping Rain water problem (https://leetcode.com/problems/trappingrainwater/) Can someone please let me know?
Yes, “Container With Most Water” and “Trapping Rain Water” are similar in that they both involve calculating the volume of water that can be stored given an array of heights. However, there are some key differences that make the problems distinct:
Problem Statement: In the “Container With Most Water” problem, you’re asked to find two lines (represented by the array values or heights) that, along with the xaxis, would form a container that can hold the most water. The water volume in this case is determined by the distance between the two lines (along the xaxis) and the height of the shorter line.
In the “Trapping Rain Water” problem, the situation is different. Here, water can be trapped between the bars in any part of the array, not just the ends. So, you’re effectively asked to find all the “valleys” or “pits” in the height array where water could be trapped.
Solution Approach: Given their different problem statements, these two problems call for different solution approaches. For “Container With Most Water”, the twopointer strategy is a common and effective approach. But for “Trapping Rain Water”, the problem can be solved using methods like dynamic programming, stacks, or two pointers (though the logic behind the twopointer approach in this problem is quite different).
In summary, while these two problems might seem similar at first glance, they require different ways of thinking and problemsolving strategies due to their differing constraints and objectives.
I think the O(n) twopointer algorithm completely misses the fact that bars may have EQUAL lengths. However, after someone pointed out you are limited from both sides (whatever it meant to say), I think the best thing one can do in the case of a tie is to move pointers from both ends until we get an increase on BOTH sides. However, this is also equivalent to moving one pointer first until we get an increase. Then, we would have to move another one, b/c it’s shorter. So, the algorithm is correct after all.
Indeed, in the case where the two bars are of equal height, it doesn’t matter which pointer you move  the area of the resulting container will be the same regardless. This is because the height of the container is determined by the shorter bar, and the width of the container decreases by one regardless of which pointer you move.
The important point to remember is that, because the height of the container is limited by the shorter bar, it’s in our best interest to try to increase the height of the shorter bar by moving its pointer. This increases the potential for finding a taller bar, which could lead to a larger area.
So in the case of a tie, moving either pointer (or both) is a valid approach. However, moving both pointers in the event of a tie doesn’t provide any additional benefit and could potentially skip over the maximum area if the next pair of bars are not equal. Hence, in the implemented algorithm, we choose to move one pointer at a time, regardless of whether the heights are equal or not.
It will be much easier to understand the solution if one recognise the loop invariant that it maintains:
maxarea >= max( what could possibly be obtained from an index that’s excluded in the loop in the entire solution space)
e.g. when you move the pointer away from index i, it’s for sure that all the possible pairs {(0, i), (1, i) … (i, i+1), … (i, n1)} gives less or equal result as maxarea.
with this in mind, it’s easy to see when pointers moved through the entire array, it’s guaranteed that maxarea is the best of the entire solution space.
Having said that, it actually took me >1hr to finally realise this property exists and thus the O(n) solution, was scratching my head among various O(n^2) methods.
You’re absolutely correct! This is a key insight into why the twopointer strategy works for this problem.
Just to expand on your explanation: when we’re using the twopointer approach for this problem, we start with the widest possible container (with the left and right pointers at the start and end of the array, respectively). This gives us the maximum possible width. We calculate the area for this container and store it as maxArea
.
We then move the pointer that is at the shorter of the two lines (since the height of the shorter line limits the amount of water the container can hold). By doing this, we’re effectively eliminating that line from consideration for future containers because any future container involving that line will necessarily be narrower, and therefore cannot hold more water (since it would be limited by the same height but have a smaller width).
This is why, as you pointed out, at any point in the iteration, the maxArea
we’ve calculated is greater than or equal to the maximum possible area for any container involving a line that we’ve moved a pointer away from.
This process guarantees that we consider all possible containers in the array, which allows us to find the maximum area in linear time. A beautiful property indeed!
What happens if it’s like 1 8 12 3 2 3 4 2 7? we would reach a point where it’s like 8 and 7, but we would have to keep moving the right pointer inwards because it will always be shorter. No?
In the case of the array [1, 8, 12, 3, 2, 3, 4, 2, 7], the twopointer approach works as follows:
Start with the widest container, which is from the leftmost bar (1) to the rightmost bar (7). The area of this container is min(1, 7) * (91) = 8.
Since the left bar (1) is shorter than the right bar (7), move the left pointer to the next bar. Now, the left bar is 8 and the right bar is still 7. The area of this container is min(8, 7) * (92) = 56, which is larger than the previous maximum area.
Again, since the left bar (8) is taller than the right bar (7), move the right pointer to the next bar. Now, the left bar is still 8, but the right bar is 2. The area of this container is min(8, 2) * (82) = 12, which is less than the previous maximum area.
Repeat this process, moving the shorter of the two pointers at each step, until the two pointers meet.
This approach works because the area of the container is limited by the shorter of the two bars. Moving the shorter pointer increases the potential for finding a taller bar, which could lead to a larger area. Even if you reach a point where the left bar (8) is taller than the right bar (7), moving the right pointer inward might help find a taller bar on the right side, increasing the possible area. The algorithm does not miss any potential maxima because it checks all pairs of bars, just in a specific order.
if the spacing between the lines vary for every pair, this algorithm wouldn’t work right?
Correct, the twopointer algorithm assumes that the spacing between each pair of lines is constant (in this case, 1). It’s essentially based on the fact that the area of the rectangle is a function of the minimum height of the two lines and the distance between them.
If the spacing between each pair of lines is variable, you’d need a different algorithm to find the maximum area. In such a case, you may need to consider all pairs of lines (i.e., use a bruteforce approach) or find some other heuristic based on the specific properties of the problem. It would certainly become a more complex problem to solve efficiently.
Can anyone tell me…we use two pointer approach only when array is sorted right? Bcoz only in that case we know that the next element or previous element is larger or smaller respectively..then how we use two pointer approach here?
The twopointer technique is often used in sorted arrays, but it’s not exclusive to them. The reason we can use the twopointer approach in this problem is due to the specific characteristics of the problem, not because the array is sorted.
In this problem, we start with the two pointers at the ends of the array. This gives us the widest possible container, and the water it can hold is determined by the height of the shorter line.
We then move the pointers inward. When moving the pointers, we move the one pointing to the shorter line because moving the pointer at the taller line would not increase the area, as the height of the container is still limited by the height of the shorter line, and the width of the container has decreased.
The key here is to understand that this approach aims to maximize the area (width * minimum height). It’s not about searching for a particular value or trying to maintain a sorted order as in some other twopointer problems. We’re just navigating the array from both ends to the center, making the best choice at each step given the problem’s constraints.
I use the same implementation in python3, but it still exceeds the time limit. The Time complex is O(n), what should I do to speed it up more?


Your code should not exceed the time limit as it is implemented in O(n) time complexity which is optimal for this problem. However, I think the problem in your code lies in the conditional block where you update the left and right pointers.
The issue is that you have used two separate if statements, which can sometimes both be true and cause unnecessary iterations. This can be avoided by using an elif or else statement. When the heights are equal, you can move either pointer. Here’s the corrected version of your code:


Now, in each iteration of the loop, exactly one of the pointers is moved. This change should prevent the time limit exceeded error.
The thing that confused me the most is that most pairs of lines are not examined. In the brute force solution we consider every possible pair but the two pointers solution considers linearly many. It is not obvious how the two pointers solution won’t miss the container with maximum area. Here is how I see it.
When we move the pointer at the shorter line, this line is never again considered in forming a container. Suppose this line is at index 3 and the line where the right pointer is is at index 7. We will never explicitly calculate the area bounded by line 3 and line 4  6. I claim that won’t cause a problem. Let me do a very coarse proof by contradiction. Without loss of generality, suppose line 3 and line 5 form the biggest area and we fail to calculate it. I will compare its area with the area formed by line 3 and line 7.
Clearly the rectangle defined by line 3, 5 has width 2, less than the width of the rectangle defined by 3, 7 The rectangle defined by line 3, 5 has height no greater than the height of line 3. The other rectangle has height equal to the height of line 3 (why? because height of line 3 <= height of line 7). Neither side of the rectangle defined by line 3, 5 is longer than that of the other rectangle so the area cannot be bigger either. This is a contradiction.
You’re absolutely correct in your analysis, and it’s an insightful way to look at this problem.
The key is realizing that moving the pointer at the shorter line can’t lead to a larger area, because the area is limited by the shorter line. As we move the pointer inward, the width is decreasing but the only chance to get a larger area is by finding a longer line.
So your ‘proof by contradiction’ is spot on, and it clearly shows why we don’t need to consider every possible pair of lines. The logic behind the two pointers solution is that it incrementally eliminates lines from consideration by always choosing to move the shorter of the two lines, which can’t contribute to a larger area. By doing so, we still guarantee that we will find the maximum possible area even though we haven’t explicitly considered every pair.
To summarize, the ’twopointer’ solution effectively prioritizes checking combinations with a wider breadth and taller height first, which ultimately helps us find the largest area.
what would be the case if the width weight is larger than 1 which means moving the short line’s pointer cannot overcome the reduction in the area caused by the width reduction?
If the width between each consecutive pair of lines is not 1 (say it’s a variable ‘w’ instead), it would make the problem more complex, and we would need to adjust our algorithm accordingly. In this case, moving the pointers inward would not necessarily be the most optimal approach, because the reduction in width might not be compensated by an increase in height.
If ‘w’ is greater than 1, for each movement, we lose ‘w’ units of width. So, the gain in height must be more than ‘w’ units to have a possibility of increasing the area. If the height difference between consecutive lines is less than ‘w’, it may be more beneficial to explore other combinations rather than moving the pointer.
Therefore, in the case of variable width weights, a simple twopointer approach may not work. You might need a different algorithm that takes into account the widths between the lines, possibly examining more combinations than the twopointer approach would. A dynamic programming approach might be more suitable in this scenario, or perhaps a variant of the twopointer method where the decision to move a pointer is based on the potential gain in area considering the width weight.
In that case, can we use sweep line algorithm?
The sweep line algorithm is a computational geometry algorithm, often used for solving problems dealing with geometric figures like lines, points, rectangles, etc. It’s commonly used for problems involving finding intersections, detecting collinear points, finding the closest pair of points, and similar problems.
In the context of this problem, where you are asked to find two lines that together with the xaxis forms a container that would contain the most water, the sweep line algorithm would not be particularly effective. This is because the sweep line algorithm is more suited for problems where spatial properties and relationships between multiple points, lines, or other geometric figures are important. The nature of this problem doesn’t fit well with the principles of a sweep line algorithm since it’s not about geometric intersections or spatial relationships, but about maximizing an area under certain conditions.
As for the case where the width weight is larger than 1, the sweep line algorithm would still not be the most optimal choice. As previously mentioned, a dynamic programming approach or an adapted twopointer method considering the width weight would likely be more suitable.
The thing I struggled to understand in some proofs was why I could not move the pointer that points to the higher line. After all, I might decrease the area temporarily but then get onto a very high line in the next step thus finding a maximum, right?
Not really… Assume there exists some biggest area between indices a and b (b > a). Let’s try to prove that always moving the pointer that points to the smaller height will eventually find our maximum area.
Since we increment pointer i (or decrement pointer j) either i will hit a first or j will hit b. We now need to guarantee that, if i hits a first, it will stay there until j hits b (or if j hits b first, it will stay there until i hits a). Say i hits a first  since we only decrement j if height[j] < height[i] we must reach b eventually  it is impossible for us to find a line higher than height[i] before reaching b because this would mean that the new area is bigger than the one between a and b, which is a contradiction.
You’ve articulated an important aspect of the twopointer technique used in solving this problem. The essential idea here is to maximize the area between the two lines (or “walls”), and the area is determined by the shorter wall and the distance between the two walls.
Let’s consider your scenario where we move the pointer at the taller wall (b in your case). Even if we encounter a taller wall in the future, the distance between the two walls has decreased, and hence, it may not necessarily lead to a larger area because the area is also dependent on the height of the other wall (a in this case). So, the crucial point here is that we’re not merely trying to find the tallest wall, but we’re trying to find the pair of walls that, when combined with their distance apart, give us the largest area.
In simpler terms, moving the pointer of the taller wall might lead us to a taller wall, but that doesn’t guarantee a larger area due to the decreased distance. This is why the algorithm opts to move the pointer of the shorter wall instead, as there’s potential to find a taller wall that could increase the area.
Your proof correctly explains why this approach works. It guarantees that, if the maximum area involves the wall at ‘a’, the left pointer will eventually reach ‘a’ and stay there until the right pointer reaches ‘b’. Similarly, if the maximum area involves the wall at ‘b’, the right pointer will eventually reach ‘b’ and stay there until the left pointer reaches ‘a’. Thus, the approach ensures all possible areas involving the maximum pair are considered.
Indeed, problems like this can sometimes feel more like puzzles than traditional coding problems. Here are a few strategies that may help when encountering these kinds of problems:
Understand the problem well: Spend some time to thoroughly understand the problem. Draw diagrams if needed. Try to come up with smaller examples and solve them manually. This could help in figuring out patterns or strategies.
Break down the problem: Many complex problems become easier when broken down into smaller, more manageable parts. This may also help you identify patterns or simpler subproblems to solve.
Consider different approaches: Try brute force first. It may not be the most efficient solution but it will give you a better understanding of the problem. From there, consider how you might optimize your solution.
Apply known techniques: If you’re familiar with common algorithms and data structures, try to apply them to the problem. Even if they don’t fit perfectly, they may lead you to a new insight.
Think of invariants: An invariant is something that does not change during the execution of your algorithm. Identifying an invariant often leads to a good strategy for solving the problem.
Practice: Lastly and most importantly, practice with a wide range of problems. The more problems you solve, the better you will get at identifying patterns and techniques to apply. LeetCode, HackerRank, CodeSignal are some platforms where you can practice a wide variety of problems.
Remember, it’s okay not to come up with an optimal solution immediately. The goal is to gradually improve your problemsolving skills over time. Don’t be too hard on yourself and keep practicing!
Here is simple explanation of why this works: Because the width is always decreasing. The only way to get a larger area is to increase the height of the rectangle. And since the height is determined by the shorter end, you move the shorter end in the hope of increasing the height of the rectangle.
You’re absolutely correct. This is a concise and intuitive way to explain the reasoning behind the twopointer technique applied to this problem.
As you’ve noted, the goal is to maximize the area of the rectangle, which is a product of its width and height. Because the width is constantly decreasing as we move the pointers towards each other, the only way to possibly increase the area is by increasing the height.
However, since the height of the water the container can hold is determined by the shorter of the two walls, we choose to move the pointer at the shorter wall in hopes of finding a taller wall and thus possibly increasing the height of our rectangle.
This technique allows us to efficiently traverse the array and find the maximum possible area.
We first find the area with the widest width. We see if this can be beaten by antoher rectangle with smaller width but bigger height.
Yes, that’s an accurate summary of the algorithm.
At the beginning, we calculate the area of the widest possible container (between the first and last wall). Then we start narrowing the width by moving the pointers inwards, but we always try to move the pointer that points to the shorter wall in hope of finding a taller one.
This way, we trade off some width for a potential gain in height, which could lead to a larger area. The process continues until the pointers meet, at which point we’ve considered all possible pairs of walls, and we can be sure that the maximum area we’ve recorded is the largest possible.
This algorithm is efficient because it avoids unnecessary calculations and iterates over the array only once. It also ensures that we don’t miss any potential maximum areas because of the way we systematically move the pointers.
It’s really like the greedy method.
Absolutely, the twopointer approach used here can indeed be considered a form of the Greedy algorithm.
In a Greedy algorithm, you make the choice that looks best at the moment to get the optimal solution for the whole problem. In this case, at each step, you’re making a “greedy” choice of moving the pointer at the shorter wall with the hope of increasing the height and thus getting a larger area. This continues until you’ve exhausted all possible choices (i.e., the pointers meet).
The important thing here is that the greedy choice works in this case because moving the pointer at the taller wall would never give a larger area. Hence, you can confidently make the greedy choice of moving the shorter wall pointer, knowing that you’re not missing out on a potentially larger area.
So, while this problem doesn’t fit the classic mold of Greedy problems (like interval scheduling, Huffman coding, or Dijkstra’s algorithm), the algorithm does embody the same principle of making a locally optimal choice at each step in the hope that these choices will lead to a global optimum.
A simple way to think of this is, the width is always decreasing. The only way to get a larger area is to see if we can increase the height. And since the height is restricted by the shorter end, you move the shorter end hoping you’d find a longer side inside
There are several other posts explaining why you should move the left pointer to the right if the left height is less, and move the right pointer to the left if the right height is less; I want to explain a simple speedup from there which took me from about 65% to 99.96% runtime.
In general, the height which is yielding our best area is larger than most, as it’s already the result of maximizations. So it becomes increasingly less likely that the height is going to increase with each step, and it’s wasteful to calculate the new area and to compare it to the best area at each step, like in most of the O(n) solutions, and is further wasteful to compare it to the height on the other side. Here I keep track of which side was active (had the lower height) and what the active height was, and as we move, I just compare the new height to it; if it’s less, as is usually the case, we keep moving, and only if it increases do we calculate the area, compare to the best area, and check for a new active side.
That’s a great insight and an effective way to improve the efficiency of the code! This essentially applies the principle of laziness or “shortcircuiting”: you avoid doing work until you’re sure you have to.
Here is a Python version of the container with most water problem, incorporating your speedup:


This code is buggy. Do not use.
Wrong Answer
51 / 61 testcases passed Input height = [1,3,2,5,25,24,5] Use Testcase Output 15 Expected 24
In this code, we maintain the current active side and active height. We only update these when we find a greater height. We also only update max_area
when we have a new active side. This means we avoid unnecessary calculations when we haven’t found a taller height yet.
The idea behind the problem is very simple. Area=height*width, you need to tradeoff between height and width to get the maximum area. For doing it so you want to decrease width with the expense of increasing height, so take two pointers one pointing to extreme left & another pointing to the extreme right now if height[l]<=height[h] increment l++ and vice versa and store the max area in each case. This will give the maximum area.
Say start and end are two points now seeing at the graph that is presented to us. We need to think about two things.
This formula should hit you up that any area between two points should be (ba)Math.min(height[b],height[a]). Now we need to think how to traverse the array. So when i see an array problem and i think about sliding window, two pointer approach first. These approaches come to you by experience, also since there are starting point and ending point two pointer will strike you easily. P.S this problem is easy to solve if you have some experience of solving array problems.
Absolutely! Familiarity with common algorithms and problemsolving techniques is a big part of becoming good at tackling these types of problems.
To reiterate the logic of the twopointer approach here:
 Initialize two pointers, one at the beginning of the array (
start
) and one at the end (end
).  Calculate the area between these two points using the formula
(end  start) * min(height[end], height[start])
.  This gives you an initial “best” area.
 Then, you move the pointers toward each other. The key insight here is to always move the pointer that currently points to the shorter line. The reason for this is that moving the pointer pointing at the taller line would never increase the area (since the height is limited by the shorter line and the width is decreasing), but moving the pointer pointing at the shorter line could potentially find a taller line and thus increase the area.
 At each step, calculate the new area and update the “best” area if the new area is larger.
 Repeat this process until the two pointers meet. At this point, you’ve considered all pairs of lines and found the maximum area.
Having experience with array problems, as you mentioned, is indeed beneficial. This familiarity can help you recognize when certain strategies like the twopointer approach might be applicable. It’s a combination of experience, practice, and understanding the underlying principles that helps in coming up with efficient solutions.
At any stage, move the higher bar inward will not help increase the area, as height will be less or equal to the lower bar, and width–, thus we have to move the lower bar inward.
You’re absolutely correct. This is the key insight behind the twopointer approach for solving this problem.
Here’s why: When using two pointers to represent the two sides of the container, the area is determined by the height of the shorter side and the distance between the two sides. If we move the pointer at the taller side, we’re not likely to find a higher bar (since we’re already at a taller one), and we are definitely reducing the distance between the two sides. This means we are not likely to increase the area.
On the other hand, if we move the pointer at the shorter side, there’s a chance we might find a taller bar, and even though we are reducing the distance between the sides, this might be offset by the increase in height, leading to a larger area. This is why we always move the pointer at the shorter side.
So in essence, the strategy is to check all possible pairs of bars, but to do so in a clever way that only requires linear time, instead of checking all pairs directly, which would require quadratic time.
The same solution in C++, but I got TLE? Any ideas?


Your current solution has a time complexity of O(n^2) because you are using two nested loops, which go through each pair of bars. This is the brute force approach and will not be efficient for larger inputs, thus resulting in Time Limit Exceeded (TLE).
Instead, you should use the twopointer approach, which has a time complexity of O(n) and is much more efficient. Here is a C++ version of the twopointer approach:


In this code, left
and right
are the two pointers. We start with left
at the beginning of the array and right
at the end. At each step, we update maxArea
to be the maximum of the current maxArea
and the area of the container formed by the bars at left
and right
. Then, we move the pointer that points to the shorter bar towards the other pointer, because this might allow us to find a taller bar that increases the area. The loop continues until left
and right
meet.
makes no sense. how do you know there isn’t some other combination that can’t be obtained by moving the pointers inward?
This is where the logic of the twopointer approach comes into play. It might seem counterintuitive at first, but let’s try to explain it step by step.
Initially, we take the widest possible container, which includes the whole array, and the height of this container is the height of the shorter line.
Any container you could possibly move to by shifting the shorter pointer inward, can’t be wider than the current container (since we’re moving inwards), and it’s also not going to be taller than the current container (since we can’t move to a container that is taller by shifting the shorter line).
Therefore, by moving the shorter line inwards, we can’t find a better potential container. That’s why we choose to move the shorter line inwards in hopes of finding a taller line.
This process is repeated until the two pointers meet, at which point all possible containers have been evaluated.
Hence, we can say with certainty that there isn’t a more optimal solution that the twopointer approach might miss, as it considers the most significant factors determining the container’s capacity – its width (distance between pointers) and its height (the height of the shorter line).
The widest container (using first and last line) is a good candidate, because of its width. Its water level is the height of the smaller one of first and last line. All other containers are less wide and thus would need a higher water level in order to hold more water. The smaller one of first and last line doesn’t support a higher water level and can thus be safely removed from further consideration.
Yes, that’s exactly right! Your understanding of the problem and the algorithm is clear and correct.
This is a critical observation for this problem. As you described, we begin with the widest possible container, with its water level determined by the shorter of the first and last line. Any other container must be less wide, so to hold more water, it must be deeper, i.e., it must have a higher water level.
And here’s the crucial insight: If a container is going to be deeper, that increased depth must come from a line taller than our current shorter line. Otherwise, the water level would remain the same or decrease, and since the width is also decreasing, the area (water capacity) would be less than our current container. So, we can safely ignore any container that includes our current shorter line, because it’s not going to help us find a deeper container.
This reasoning leads us directly to the twopointer algorithm. We move the pointer at the shorter line inwards, to look for a taller line that might allow a higher water level. We continue this process, adjusting our pointers and maximum area as necessary, until the pointers meet, at which point we’ve considered every possible container.
Variables i and j define the container under consideration. We initialize them to first and last line, meaning the widest container. Variable water will keep track of the highest amount of water we managed so far. We compute j  i, the width of the current container, and min(height[i], height[j]), the water level that this container can support. Multiply them to get how much water this container can hold, and update water accordingly. Next remove the smaller one of the two lines from consideration, as justified above in “Idea / Proof”. Continue until there is nothing left to consider, then return the result.
“because of its width. Its water level is the height of the smaller one of first and last line.”
Q: Why the height of water level is not the shortest line within (first, last) ? （According to Cask Effect）
The problem isn’t asking for the maximum amount of water that could be held if we poured water into the container and let it settle, as in the Cask Effect or the Trapping Rain Water problem.
Instead, this problem is asking for the maximum amount of water that could be held in a container formed by two lines and the xaxis, i.e., the area of the rectangle formed by those two lines.
In this scenario, the height of the rectangle (and therefore the maximum water level) is determined by the shorter of the two lines forming the sides of the rectangle. Water will spill over the shorter line if we try to fill past its height, even if the other line is taller. So, the maximum water level is indeed the height of the shorter line, regardless of the heights of any lines inside the container.
The shorter of the first and last line, then, determines the maximum water level for the widest possible container.
We start greedy by maximizing width first, setting left and right pointers at their extremes. Shrinking pointers in every direction, all other things being equal, is a cost: 900 * 900 * 1000 is less than 900 * 900 * 999. The way we minimize this cost is yet again by being greedy  by moving the pointer in the direction which minimizes the bottleneck. If we’re bottlenecked by the left pointer we take our chances and move it to see if our position improves by moving it, and vice versa. Say, if we have a 5 * 1,000,000,000 * 1000 scenario, it is immediately obvious that we’re better off shrinking out pointer from the side of 5 than from the side of 1,000,000,000, as even a very slight variation in the bottleneck changes the total amount of water we have by a huge factor. In fact, this would hold true all the way until we hit a number which is higher than 1,000,000,000 from the left side, if this situation happens at all. We do this until we exhaust the logical range of possibilities that our maximum amount of water in principle can be made better  until the left and right pointers collide.
Yes, exactly! That’s a great summary of the logic behind this twopointer approach.
This problem is a great example of how a greedy algorithm can be used effectively. By starting with the widest possible container (i.e., the distance between the two ends of the array), and then iteratively moving the pointer at the shorter height inward, we are effectively exploring all possible container sizes in a systematic way.
At each step, the container’s width decreases, but its height might increase. And we’re looking for the maximum of the product of height and width, i.e., the maximum area.
If we moved the taller line inward, we would be decreasing both the height and the width, which would never lead to a larger area. But by moving the shorter line inward, we keep the possibility of finding a taller line and thus a larger area.
We stop when the two pointers meet, which ensures that we’ve considered all pairs of lines. This approach therefore guarantees finding the container that can hold the most water.
Good job on understanding and summarizing the concept! This kind of thinking is indeed very crucial for tackling algorithmic problems.
The Two pointer method would start from extremes, and only move the smaller height index inwards as that is the one limiting the area. If by chance we get a bigger height, even though the width is now less, we can have higher area. Track the highest area calculated till now and return it.
The current formulation of the problem doesn’t make any sense. After having seen the solution, I’ve found the following clarification to be needed:
Forget about water; that only confuses things. The problem is just about finding a maximum area rectangle. The water formulation made me think that for there to be a container, all lines between the container ends must be lower than the ends. I didn’t get to see examples of this not being the case because my solution timed out.
Also, the formulation doesn’t tell what the function should actually return. A more correct formulation would thus be something like:
“[…]. All pairs of the n lines define a rectangle with a height given by the shorter line and a width given by the distance between the lines. Return the area of the rectangle with the largest area.”
You’re correct that the problem is ultimately about maximizing the area of a rectangle under a set of constraints, and that the “water” analogy can make it seem more complex than it actually is. Your reformulation of the problem statement certainly helps to clarify what the function needs to achieve.
However, it’s worth noting that these problem statements are often intentionally designed to test a candidate’s ability to interpret and work with realworld scenarios. The “water” analogy might seem confusing at first, but it actually provides a useful conceptual framework for understanding the problem. The key is to translate this realworld scenario into a more abstract mathematical problem that you can solve algorithmically. That being said, understanding the problem well is the first step to finding a solution, so it’s great that you’ve found a way to make the problem statement clearer for yourself.
As for what the function should return, in the original problem statement, it’s mentioned that you need to “return the maximum area of the container.” This is a standard way to indicate that the function should return the maximum area that can be achieved under the given constraints.
I hope this perspective is helpful! It’s always great to critically analyze problem statements and find ways to make them clearer. It’s a skill that will serve you well in all kinds of problemsolving scenarios.
Explanation
To start, we place both pointers at the beginning and end of the array. While this is common with most two pointer approaches, in this specific problem it allows us to consider all potential possibilities. Thus, in a way we are still brute forcing the solution, however we are carefully optimizing the possibilities at each iteration so that the optimal solution is not missed. At each iteration, no matter what, the width will decrease. This is because we must move either the left and right pointer. Therefore, we must consider the potential outcomes after we either increase the left pointer or decrease the right pointer. Outcome A: If we decrease a pointer and the height of the pillar is smaller than the previous height (where the pointer was originally before we moved it), the overall area will have to be smaller. This is because we take the minumum of the two heights that the pointers are pointing at, and because the width will be decreased by 1, the new area will have to be the same or smaller. Outcome B: If we decrease a pointer and the height of the pillar is larger or the same as the previous height, the width may or may not be larger than the previous height, because it depends on whether the height difference between the new pillar and the previous pillar can compensate for the loss of width. So why do we always move the pointer of the smallest pillar?
The reason for this is simple. There is no reason to hang onto the pillar of the smallest size in the current range because in the next iteration, the overall width will decrease by one. Thus, a potential greater area will never be reached by hanging onto the smaller pillar, because even if another taller pillar is found by moving the pointer at the taller pillar, the area must always be calculated with the minimum of the two pillars. Due to the smaller width by decreasing the pointer range, the overall area will be smaller regardless. Thus, we can safely disregard the smaller pillar at each iteration.
Exactly, you’ve summarized the approach quite accurately. The key aspect of the twopointer strategy in this problem is to initially set pointers at the widest possible distance, then always moving the pointer at the smaller line inward. Here’s why this approach works:
 It starts with the maximum width container and explores all possible heights, effectively ensuring that if a higher line is found, the area might increase despite the width decrease.
 On each iteration, by moving the pointer of the smaller line, we are effectively trying to find a line which is longer than the smaller one. If we find such a line, it’s possible that it could form a larger area with the other end.
 If we moved the pointer at the larger line, we could neither gain a longer line (since we’re moving away from the longer line) nor a wider container (since the width is reducing on each step). So, moving the pointer at the larger line could never yield a better solution.
Through this process, the algorithm effectively evaluates all promising candidates for the largest container and ensures the largest one is found, all while maintaining a linear time complexity.
But remember, coming up with such solutions requires a good understanding of the problem at hand and practice with similar problems or strategies (like twopointer approach or greedy algorithms).
What if the distances between pillars are not constant?
The solution mentioned above applies to the case where the distances between pillars are constant. This is because the twopointer approach is designed to maximize the width (distance) first, and then aim to increase the height (pillar size).
If the distances between pillars are not constant, the solution would become more complex. A brute force approach would still check every possible pair of pillars, but the time complexity would be higher.
Alternatively, you might also use a variant of the twopointer approach, but you would need to adjust your strategy when deciding which pointer to move. Instead of just considering the heights of the pillars, you would also need to take into account the distances between them.
For example, moving the pointer at the shorter pillar might not always be the best choice if the distance to the next pillar is significantly larger. You might get a larger area by moving the pointer at the taller pillar if the distance to the next pillar is small. However, finding an optimal strategy for this variant of the problem could be quite challenging.
In general, problems involving nonuniform distances or weights often require more complex algorithms and may not have efficient solutions. They may involve techniques like dynamic programming, graph algorithms, or other more advanced methods.
WA Input: [1,2,1] Output: 1 Expected: 2 Does this mean to find the total “area”? NO
WA Input: [1,2,4,3] Output: 6 Expected: 4 This is to find the max area between any of two lines.
It seems like there might be some misunderstanding about the problem. Here’s the correct interpretation of those test cases:
Input: [1,2,1] The output should be 2, not 1. The maximum area of water the container can hold is determined by the height of the shorter line (1) and the distance between the two lines (2), so 1*2 = 2. Here, the lines are at index 0 and index 2, each with a height of 1.
Input: [1,2,4,3] The output should be 4, not 6. Here, the maximum area is determined by the lines at index 1 and 2. The height of the shorter line is 2, and the distance between these two lines is 1. Therefore, the maximum area is 2*1 = 2. But if you consider the lines at index 2 (height 4) and index 3 (height 3), the area is min(4,3)1 = 31 = 3. The maximum among all these possible areas is 3.
So the goal of this problem is to find two lines, which, together with the xaxis, form a container that can hold the most water. The height of the container is determined by the shorter of the two lines, and the width of the container is the distance between the two lines. You are to return the maximum possible area (height x width) that can be obtained from such a setup.
One subtlety about the greedy approach is how to make sure it doesn’t miss out the global optimal value. For example, if we finally arrive at left=20, right=21 but the actual optimal pair is left=5, right=10 , it would mean our answer is strictly suboptimal. From a mathematical point of view, it isn’t totally clear how the twopointer incrementdecrement approach can actually avoid the possibility of strict suboptimality.
This is a valid concern, but here’s why the twopointer approach works and is not suboptimal for this problem:
The twopointer approach starts with the widest possible container and keeps narrowing it down while trying to increase the height. The aim of this approach is to maximize the area which is the product of width and the minimum height of the two lines forming the container.
In the case of left=5 and right=10 being the optimal pair, the twopointer approach would have already considered this pairing when the right pointer was at 10 and the left pointer was moving from left towards right.
When the pointers are at left=20 and right=21, it means that the twopointer approach has explored all the possibilities for wider containers and couldn’t find a container with greater area. Thus, the container formed by left=20 and right=21 must have a greater or equal height as compared to the one formed by left=5 and right=10 to result in a greater or equal area, as the width has been reduced.
The key here is that while the width is decreasing as we move the pointers, we’re always trying to find a higher line which could potentially offset the reduction in width and give a greater area.
Therefore, the twopointer approach won’t miss the global optimal value and isn’t suboptimal for this problem.
Tidbits
It’s really like the greedy method.
Simple Explanation of the Proof
Let’s explain the proof for the twopointer algorithm used in solving the “Container With Most Water” problem, in a way that should be easily understandable.
So, the algorithm starts with two pointers, one at the beginning and one at the end of the array, and it tries to calculate the area of water that can be contained between these two points. The area is calculated as the product of the distance between the two pointers and the height of the shorter line.
The goal of the algorithm is to find the maximum area. So, once it calculates an area, it then tries to see if it can find a bigger one. How does it do this? Well, it moves the pointer at the shorter line towards the other one. It does this because it’s looking to find a taller line that could potentially give a bigger area.
But why does it only move the pointer at the shorter line? Why not move the pointer at the taller line?
Think of it this way: The water container’s capacity is always limited by the shorter line, right? So, even if we move the pointer at the taller line and find an even taller line, it wouldn’t change the fact that the capacity is still limited by the shorter line.
However, if we move the pointer at the shorter line, and we find a taller line, then we have a chance to get a bigger area, because now the limiting factor (the height of the shorter line) has increased.
And this is how the algorithm makes sure it checks all potential containers that could possibly have the maximum area. It keeps moving the pointers until they meet, which ensures that all pairs of lines have been considered.
This approach guarantees that the maximum area of water that can be contained is found, as every possible pair of lines is checked.
So, that’s a simple way to understand the proof of the algorithm. It’s essentially about strategically moving the pointers to check all possible areas, while maximizing the chances of finding a larger area.
Similar Problems
Here are 10 problems from LeetCode that require similar problemsolving strategies or use similar underlying concepts as the problem we’ve just solved:
LeetCode 167. Two Sum II  Input array is sorted: This problem also involves using two pointers from both ends of the array, similar to the problem we just solved. The approach is to gradually shrink the search space by moving the pointers.
LeetCode 88. Merge Sorted Array: This problem also uses a twopointer approach to merge two sorted arrays.
LeetCode 15. 3Sum: This problem is an extension of our problem where instead of two pointers, we use three pointers to find three numbers such that their sum is zero.
LeetCode 76. Minimum Window Substring: This problem involves using two pointers to track the window of characters in a string, similar to tracking the maximum water container in our problem.
LeetCode 26. Remove Duplicates from Sorted Array: Here, two pointers are used to track duplicates in a sorted array, which involves a similar approach to how we managed pointers in our original problem.
LeetCode 27. Remove Element: This problem involves using two pointers where one is used for iterating over the array and the other for tracking the next position to replace, similar to how we moved the
left
andright
pointers.LeetCode 283. Move Zeroes: Similar to our problem, this question also requires moving the pointers based on certain conditions.
LeetCode 75. Sort Colors: This problem requires a threepointer technique, which is an extension of the twopointer technique used in our problem.
LeetCode 80. Remove Duplicates from Sorted Array II: The problem also uses a twopointer approach to maintain the maximum of two duplicates in a sorted array.
LeetCode 11. Container With Most Water: This problem is identical to the one we’ve just solved, requiring a twopointer technique to find the maximum area.
All of these problems share the twopointer or multiplepointer technique, which we used in our solution. In some problems, the concept of moving the pointer based on certain conditions is also shared with our problem. These similarities make these problems relevant for someone who has just solved the container with most water problem.
Language Agnostic Coding Drills
Understanding of variables: Learn how to declare and assign values to variables. Practice creating several variables and changing their values.
Understanding of lists: Learn what lists are and how to create them. Understand how to access individual elements in a list and how to find the length of a list.
Understanding of basic arithmetic operations: Learn and practice with basic arithmetic operations such as addition, subtraction, multiplication, and division.
Understanding of while loops: Understand the structure of a while loop and how it works. Practice writing code with while loops.
Understanding of ifelse statements: Understand the concept of conditionals. Learn how to write and use ifelse statements.
Understanding the min function: Learn and understand the concept of a minimum function that returns the smallest of all input values.
Understanding the max function: Learn and understand the concept of a maximum function that returns the largest of all input values.
Understanding of comparison operators: Understand and practice with comparison operators like <, >, <=, >=, ==, !=.
Incrementing and decrementing values: Understand the concept of incrementing and decrementing values. Practice incrementing or decrementing values.
Understanding classes and methods: Learn what classes and methods are. Understand how to define a class and create methods inside it.
Understanding method parameters and return values: Learn what method parameters and return values are. Practice writing methods that take parameters and return values.
Understanding complex expressions: Practice creating and evaluating complex expressions, like
(r  l) * min(height[l], height[r])
andmax(area, (r  l) * min(height[l], height[r]))
.Understanding list indexing: Practice accessing elements from a list using an index.
Understanding the concept of problem solving using two pointers: This is a technique often used in array/list problems, where we start with one pointer at the beginning of the array, and one at the end, and then move them towards each other while solving the problem. This concept is used in the given code.
Understanding the problem domain  container with most water: This is the specific problem that the given code is solving. It involves understanding and implementing the logic to solve the problem: finding two lines, which, together with the xaxis forms a container, such that the container contains the most water.
Targeted Drills in Python
Let’s create some drills in Python for each of the concepts identified:
Drill 1  Understanding of variables


Drill 2  Understanding of lists


Drill 3  Understanding of basic arithmetic operations


Drill 4  Understanding of while loops


Drill 5  Understanding of ifelse statements


Drill 6  Understanding the min function


Drill 7  Understanding the max function


Drill 8  Understanding of comparison operators


Drill 9  Incrementing and decrementing values


Drill 10  Understanding classes and methods


Drill 11  Understanding method parameters and return values


Drill 12  Understanding complex expressions


Drill 13  Understanding list indexing


Drill 14  Understanding the concept of problem solving using two pointers


Advanced Drills
Let’s break down the concept of “container with most water” problem into smaller units:
Drill 15.1  Understanding the problem statement
First, we need to understand the problem statement of “container with most water”. In this problem, you’re given an array representing a series of vertical lines on a graph. The distance between each line represents the width of the container, and the height of the line represents the height of the container. The task is to find the maximum area of the container.
There’s no direct coding involved in this step, but to help understand the problem, try drawing a few arrays as vertical lines on a graph and visually identifying which containers would have the most water.
Drill 15.2  Initializing the pointers
In the beginning, we can assume the container with most water is formed by the first and last lines. So we initialize two pointers, one at the beginning of the array and one at the end.


Drill 15.3  Calculating the area
We calculate the area by finding the minimum of the two lines forming the container, multiplying it by the distance between them, and store it as our current maximum area.


Drill 15.4  Moving the pointers
We then move the pointers towards each other. If the height at the left pointer is less than the height at the right pointer, we move the left pointer one step to the right. If the height at the right pointer is less or equal, we move the right pointer one step to the left.


Drill 15.5  Iterating until pointers meet
We keep repeating steps 15.3 and 15.4 until the two pointers meet.


The above code represents the entire solution, but broken down into separate steps.