Maximum Earnings From Taxi
This is a classic optimization problem where you want to maximize the profit by choosing the best rides. A dynamic programming approach works here.
Let’s use a dynamic programming array dp
of size n + 1
where dp[i]
represents the maximum profit we can make when we are at point i
.
For each ride, we update the dp
value at the endpoint of the ride, considering if taking that ride is beneficial or not. Specifically, if taking a ride ending at point i
gives more profit than not taking it, we update dp[i]
.
To solve this efficiently, we will sort the rides based on their end point, so that we can process them in increasing order of their end point. This ensures that by the time we process a ride, we have already computed the optimal profit for all previous points.
Python solution:


This solution uses dynamic programming to compute the maximum profit at each point on the road. By processing the rides in increasing order of their end point, it ensures that we have already computed the best profit for all previous points when considering a particular ride.
Identifying Problem Isomorphism
“Maximum Earnings From Taxi” is approximately isomorphic to “Weighted Job Scheduling”.
Reasoning:
In the “Maximum Earnings From Taxi” problem, you are trying to select rides (jobs) to maximize the total earnings. The challenge is that you can only take one ride at a time and rides have a start and end time.
Similarly, in the “Weighted Job Scheduling” problem, you are given a set of jobs where each job includes a start time, an end time, and a profit. The task is to find the most profitable set of jobs you can do assuming that you can only work on one job at a time.
In both problems, the task involves selecting jobs (or rides) in a way that maximizes the total profit (or earnings) and there’s the constraint that you can only do one job (or ride) at a time. Both problems can be solved by dynamic programming, where you keep track of the maximum profit at each step and use that to compute the maximum profit for the next step.
“Maximum Earnings From Taxi” is simpler because it only involves one array of jobs (rides), and the profit for each ride is simply the end point minus the start point plus the tip. On the other hand, the “Weighted Job Scheduling” problem involves determining the maximum profit at each step, which adds some complexity.
The mapping is approximate due to the different context and specifics of the two problems. In “Weighted Job Scheduling”, the jobs are not necessarily sorted and you have to sort them. In “Maximum Earnings From Taxi”, the rides are already sorted by the end point. The way profits are calculated in both problems is also different.
10 Prerequisite LeetCode Problems
This involves Dynamic Programming (DP) and sorting. Here are 10 problems that cover these concepts:
LeetCode 322: Coin Change
Introduces to you the basic concept of Dynamic Programming for optimization problems.LeetCode 300: Longest Increasing Subsequence
Another Dynamic Programming problem that deals with sequences, like the taxi problem.LeetCode 646: Maximum Length of Pair Chain
A problem that involves sorting and then applying Dynamic Programming.LeetCode 139: Word Break
Dynamic Programming problem with a focus on using previously computed results.LeetCode 70: Climbing Stairs
Basic introduction to DP where you learn to build the solution using previous results.LeetCode 1143: Longest Common Subsequence
Teaches how to approach DP problems dealing with two sequences.LeetCode 198: House Robber
A simple yet effective problem to understand Dynamic Programming.LeetCode 376: Wiggle Subsequence
This problem also deals with sequences, requiring sorting and DP for the solution.LeetCode 1048: Longest String Chain
The problem involves sorting and then applying dynamic programming, similar to the taxi problem.LeetCode 518: Coin Change 2
This problem is a more complex version of the coin change problem involving DP.
Clarification Questions
Overlapping Rides: Is it possible for two or more rides to have overlapping start and end points?
Multiple Rides at Same Point: Can multiple passengers be picked up from the same point?
Initial Position: Do we always start at point 1 or could the taxi start at a different point?
Travel Time: Is the time to travel between points relevant to the problem?
Ordering of
rides
Array: Is therides
array sorted in any particular order, like by start point or end point?Empty Taxi Requirement: Is the taxi required to be empty at the final destination (point
n
)?Empty
rides
Array: What should be the output if therides
array is empty?Tiebreaking: If there are multiple optimal solutions that yield the same maximum dollars, is any particular one preferred?
Negative or Zero Tips: Are negative or zero tips possible, even though the problem states tips are greater than or equal to 1?
Minimum Rides: Is the taxi required to pick up a minimum number of passengers, or can it choose not to pick up anyone?
These clarification questions can help better understand the constraints and nuances of the problem.
Problem Analysis and Key Insights
Maximization Goal: The objective is to maximize the total dollars earned, which hints at an optimization problem. You’re not just tracking a state; you’re optimizing it.
Single Passenger Rule: You can only carry one passenger at a time. This eliminates the complexity of considering multiple passengers simultaneously.
Direction Constraint: You can only move from point 1 to point n, and you can’t change direction. This simplifies path planning as you don’t need to consider going back.
Earnings Formula: The dollars earned from a ride is not just the tip; it’s
endi  starti + tipi
. Therefore, longer rides are not necessarily less attractive.StartEnd Constraint: Each ride has a unique start and end point, with
starti < endi
. You don’t have to consider rides that begin and end at the same point.Multiple Choices at a Point: At each point, you may have multiple rides to choose from. Picking the right one is key to maximizing earnings.
Drop and Pick: You may drop off a passenger and pick up a different one at the same point, which gives you more flexibility and options.
Fixed End Point: The final destination is always point
n
, which limits the search space for the solution.
These insights can help guide the problemsolving approach, as they outline what elements are essential for crafting an optimal solution.
Problem Boundary
The scope of this problem involves:
Input Range: The problem constrains the number of points (
n
) and the number of rides (rides.length
), which provides an upper limit on the computational complexity we should expect.Objective: The goal is to find the maximum earnings possible, given the input rides and constraints. It’s not about finding all possible ride combinations or paths.
Algorithms and Data Structures: The problem implicitly suggests the need for sorting, dynamic programming, or greedy algorithms to achieve an optimal solution within the given input constraints.
Single Scenario: The problem is confined to a single, continuous journey from point 1 to point n. There are no multiple trips or days involved.
No External Factors: There’s no mention of fuel costs, time limitations, or other external conditions that could affect the earnings.
Deterministic Earnings: The earning from each ride is determined and not variable. There are no probabilities involved.
State Transitions: Your taxi can pick up and drop off passengers at any given point, but it can only move in one direction—forward.
Output: The output is a single integer value that represents the maximum earnings possible. No other information is requested.
Functional Requirement: This is a pure computational problem with no interactive or realtime requirements.
The scope is clearly defined, allowing us to focus on solving the specific optimization problem described, without the distraction of external variables or conditions.
Establishing the boundary of this problem involves identifying the limits or edges within which the problem exists and needs to be solved. Here are some ways to delineate those boundaries:
Input Constraints:
n
is between 1 and 10^5rides.length
is between 1 and 3 * 10^4starti < endi
and both are within the range ofn
tipi
is between 1 and 10^5
Ride Rules:
 You can only move forward from point 1 to point
n
.  Only one passenger can be in the taxi at any given time.
 You can pick up and drop off a passenger at the same point.
 You can only move forward from point 1 to point
Objective Boundary:
 The focus is purely on maximizing earnings, not on minimizing time, fuel, or other metrics.
Algorithmic Boundaries:
 Given the input constraints, the algorithm needs to run in a time complexity that can handle the upper limits efficiently.
State Limits:
 You start at point 1 and must end at point
n
.  You can’t change direction; you can only move from a lowernumbered point to a highernumbered one.
 You start at point 1 and must end at point
Output Boundary:
 The output is a single integer, which is the maximum number of dollars you can earn.
Functionality:
 The problem is a standalone, pure computational problem with no dependencies on external systems or realworld timing.
By understanding these boundaries, we can more effectively focus our problemsolving effort and rule out extraneous concerns. This makes it clearer what the algorithm needs to consider and what it can safely ignore.
Problem Classification
The problem belongs to the domain of Dynamic Programming and Optimization. It also involves Array manipulation.
What
n points on a road: These are the locations between which the taxi can travel.
rides array: Contains tuples with information about the passengers (
starti
,endi
,tipi
).Maximize Earnings: The objective is to pick rides in a way that maximizes earnings, calculated as
endi  starti + tipi
.Optimization Problem: You’re looking to find the combination of rides that maximizes your earnings.
Constraintbased: The constraint here is that you can only pick one passenger at a time and must follow the given road points in one direction.
Sequential Decisionmaking: You have to decide in a sequence which passenger to pick up next based on the point you are currently at, which aligns with dynamic programming.
The problem involves making choices at each point to maximize a certain value, adhering to constraints, which is a classic hallmark of optimization problems often solved using dynamic programming.
Distilling the Problem to Its Core Elements
Fundamental Concept:
 The fundamental concept here is “Dynamic Programming.” Specifically, it’s about finding the optimal sequence of choices to maximize a particular value—in this case, earnings from rides.
Simplest Description:
 Imagine you’re a taxi driver on a oneway street with several possible passengers along the way. Each passenger wants to go from one point to another and will give you a tip. You can only take one passenger at a time, and you aim to make as much money as possible by the time you reach the end of the road.
Core Problem:
 The core problem is to find the sequence of picking up and dropping off passengers that maximizes your total earnings, considering both the fare and the tip.
Key Components:
 Starting and ending points: Where each ride starts and ends.
 Tips: Additional earnings for each ride.
 Sequence of rides: The order in which to pick up passengers to maximize earnings.
 Constraints: One passenger at a time, oneway direction from point 1 to point
n
.
Minimal Set of Operations:
 Sort the Rides: Based on starting or ending points for easy access and comparison.
 Track Earnings: Keep a record of the maximum earnings possible at each point along the route.
 Update Earnings: When reaching a new point, update the maximum earnings you can make based on the previous points and the new passenger options.
 Find Max Earning: At the end (point
n
), the maximum earning would be your answer.
By understanding these elements, you can focus your approach on solving the core problem efficiently.
Visual Model of the Problem
Visualizing this problem can be done in several ways. Here are a few methods:
Number Line:
 Represent the road as a number line from 1 to
n
.  Mark the starting and ending points of each ride on this line.
 Annotate these points with the tip associated with each ride.
 Represent the road as a number line from 1 to
Table or Spreadsheet:
 Create a table where each row corresponds to a ride.
 Columns could be “Start Point,” “End Point,” and “Tip.”
 Sorting the table by one of these columns can give you a clear picture of your options at each step.
Timeline:
 Think of time progressing as you move from point 1 to
n
.  On this timeline, mark when you would pick up and drop off each passenger and the money you’d earn for each.
 Think of time progressing as you move from point 1 to
Tree Diagram:
 Each node could represent a decision point—either picking up a passenger or skipping.
 Each path from the root to a leaf could represent a sequence of such decisions, and the leaf could show the total earnings for that sequence.
Graph:
 If you’re more comfortable with graph theory, you can also represent the problem as a directed graph.
 Each node is a point on the road, and each edge could represent a ride, annotated with its earnings (distance + tip).
These visualizations can help clarify your options at each point on the road and make the dynamic programming approach more intuitive.
Problem Restatement
You’re a taxi driver on a oneway road with points labeled from 1 to n
. You start at point 1 and aim to reach point n
. Passengers are scattered along this road, and each one has a starting point, an ending point, and a tip they’re willing to give. You can only carry one passenger at a time, and you can’t change your driving direction. Your goal is to make the most money possible by picking up and dropping off passengers as you move from point 1 to point n
.
Requirements:
 You start at point 1 and go up to point
n
.  Each ride has a starting point, an ending point, and a tip.
 You can only carry one passenger at a time.
 The money you make from each passenger is the sum of the distance driven and the tip given.
Constraints:
 1 <= n <= 105
 1 <= rides.length <= 3 * 104
 1 <= starti < endi <= n
 1 <= tipi <= 105
Your task is to maximize your earnings.
Abstract Representation of the Problem
This problem is about optimizing resource allocation within constraints. The resource here is your time and capacity (one passenger at a time), and the constraint is the oneway directionality of the road.
In abstract terms, you have:
 A sequence of points
(1, 2, 3, ..., n)
representing locations.  A set of tasks, where each task is a tuple
(start, end, reward)
.  An objective function to maximize, which is the sum of the rewards you collect by completing tasks.
Constraints:
 You must move from the first to the last point in the sequence.
 You can only pick one task at any given time.
 You can pick up and drop off a task only at its specified
start
andend
points.
The objective is to find the sequence of tasks that maximizes the total reward while adhering to these constraints.
Terminology
Resource Allocation: This term is often used in optimization problems to describe the efficient distribution of available resources. In this problem, the resource is the taxi’s time and space for one passenger.
Objective Function: This is a function that you aim to maximize or minimize. Here, the objective function is the total amount of money earned.
Constraints: These are the conditions or limitations imposed on a problem. The constraints here include the oneway directionality of the road and the limitation of carrying only one passenger at a time.
Tuple: In computer science, a tuple is an ordered list of elements. Each ride is represented as a tuple
(start, end, tip)
.Optimization: This is the process of choosing the best solution from a set of possible solutions. Here, you’re optimizing the sequence of rides to maximize your earnings.
Dynamic Programming: This is a method for solving complex problems by breaking them down into smaller subproblems. While not explicitly mentioned, this problem is likely best solved using dynamic programming.
Each of these terms helps frame the problem or could be important in describing a potential solution. Understanding them will make it easier to grasp the problem’s intricacies and constraints.
Problem Simplification and Explanation
You’re a taxi driver on a oneway road with several stops. At each stop, there might be passengers waiting. Each passenger wants to go from one stop to another and is willing to tip you a certain amount. Your goal is to make the most money possible by picking up and dropping off passengers. You can only take one passenger at a time and can’t turn around.
Key Concepts:
Oneway Road: Think of this like a oneway conveyor belt; you can only move forward.
Stops: These are places where you can pick up or drop off a passenger. They are ordered sequentially.
Passenger Requests: Each request has a start stop, end stop, and tip. This is the passenger telling you, “If you take me from A to B, I’ll tip you $X.”
Maximum Earnings: You want to make as much money as possible, considering both the distance traveled and tips received.
Interaction:
The interaction is between you, the stops, and the passenger requests. You have to decide whom to pick up and drop off at each stop to maximize your earnings.
Metaphor:
Imagine you’re a kid with a lemonade stand on a street where people only walk in one direction. People tell you in advance, “If you can bring me a lemonade to the end of the street, I’ll give you a tip.” Some might offer a better tip than others, and some might be going farther down the street. You have to figure out how to maximize your tips, but you can only deliver one cup at a time and only move in one direction down the street.
Breaking down the problem like this should make it easier to think about how to solve it.
Constraints
Let’s delve into elements that could be exploited for an efficient solution:
Oneway Road: The road is oneway. This simplifies our choices. We don’t need to consider turning back, which narrows down our decisionmaking.
Limited Passenger Load: You can only take one passenger at a time. This limitation can actually simplify the problem because you don’t need to consider combinations of passengers.
Ordering of Stops: Stops are labeled sequentially, which gives us a natural sorting order. This could make it easier to plan your pickups and dropoffs.
Numerical Constraints: The problem constraints (like
1 <= n <= 105
,1 <= rides.length <= 3 * 104
, etc.) give us an upper bound on how large the input can be. This is crucial for considering the time complexity of our algorithm.Fixed Tips: Each ride has a fixed tip, not a range or a percentage. This makes it straightforward to calculate the benefit of each possible ride without additional computation.
Start and End Points: Since each passenger has a clearly defined start and end point, we could use this to sort or group passengers, making it easier to decide whom to pick up next.
No Overlapping Constraint: You may drop off a passenger and pick up another at the same point. This condition can be exploited to maximize profit without moving the taxi unnecessarily.
Identifying these characteristics should help in formulating an efficient approach to solve the problem.
Analyzing the constraints yields several key insights:
Bounded Number of Points: With
n
ranging from 1 to 105, the number of points on the road is limited. This constraint makes it plausible to consider solutions that might involve iterating through all points, although we should aim for efficiency.Limited Rides: The maximum number of rides is 3 * 104, which is not exceedingly high. This suggests that we might be able to sort or even preprocess the rides in some way without running into performance issues.
Fixed Ride Length: Each ride has a distinct start and end point with
starti < endi
. This suggests that each ride will have a positive length, eliminating edge cases where start and end are the same.Tips Range: The tip for each ride ranges from 1 to 105. Since the tips are bounded and integers, we can perform precise calculations on them.
No Negative Values: All the input values are positive integers, ruling out edge cases involving negative or zero values.
Oneway Direction: Given that the direction is oneway and you can’t change it, the problem becomes simpler in terms of planning the route. We only need to consider progressing from lowernumbered to highernumbered points.
Single Passenger Limit: The constraint that only one passenger can be in the taxi at any time simplifies the state we need to manage.
Drop and Pick at Same Point: The fact that you can drop off and pick up another passenger at the same point gives us more flexibility in designing an efficient algorithm.
Understanding these constraints can guide us in choosing an appropriate data structure and algorithmic approach, ultimately leading to a more efficient solution.
Case Analysis
Certainly, let’s explore different test cases to better understand the problem.
Test Case Categories:
 Minimum Constraints (Edge Case)
 Single Best Route
 Multiple Routes, Single Best Answer
 Equal Profit from Different Rides
 Gaps in Points with No Rides
 Multiple Rides Starting from the Same Point
1. Minimum Constraints (Edge Case)
 Input:
n = 1, rides = []
 Expected Output:
0
 Analysis: The minimum values for the constraints. No rides are available, and the taxi is already at point
n
.
2. Single Best Route
 Input:
n = 5, rides = [[1, 5, 3]]
 Expected Output:
7
 Analysis: Only one ride is available which is also the most profitable, making it the clear choice.
3. Multiple Routes, Single Best Answer
 Input:
n = 5, rides = [[1, 3, 2], [2, 5, 2], [3, 5, 3]]
 Expected Output:
6
 Analysis: Multiple routes are available but taking the third ride from point 3 to point 5 yields the most profit.
4. Equal Profit from Different Rides
 Input:
n = 5, rides = [[1, 3, 2], [2, 4, 2]]
 Expected Output:
4
 Analysis: Both rides offer the same profit. We can choose either.
5. Gaps in Points with No Rides
 Input:
n = 10, rides = [[1, 2, 2], [7, 10, 2]]
 Expected Output:
6
 Analysis: The gaps in points don’t offer rides. This test ensures the solution accounts for such scenarios.
6. Multiple Rides Starting from the Same Point
 Input:
n = 5, rides = [[1, 2, 1], [1, 3, 2], [1, 5, 2]]
 Expected Output:
4
 Analysis: Multiple rides start from the same point. The algorithm should select the most profitable one, in this case, the ride from point 1 to point 5.
Edge Cases
 The first test case with minimum constraints is an edge case.
 Another edge case would be having all rides start and end at the same points, forcing the algorithm to select the most profitable one among them.
These test cases aim to cover various aspects of the problem, including the constraints and potential pitfalls, to ensure a robust solution.
Visualizing these cases can help in understanding the problem more intuitively. Here’s how you might visualize each test category:
Minimum Constraints (Edge Case)
 A single point
1
on a line. No passengers, so no additional points.
1
 A single point
Single Best Route
 Points
1 to 5
in a line. A single passenger ride between1 and 5
.
15 (ride)
 Points
Multiple Routes, Single Best Answer
 Points
1 to 5
in a line. Multiple rides between various points.
135    r1 r2 r3
 Points
Equal Profit from Different Rides
 Points
1 to 4
on a line. Two rides with equal profit.
134   r1 r2
 Points
Gaps in Points with No Rides
 Points
1 to 10
on a line. Rides only at some segments.
12710   r1 r2
 Points
Multiple Rides Starting from the Same Point
 Points
1 to 5
on a line. Multiple rides start from point1
.
15    r1 r2 r3
 Points
In these visualizations, the dashes 
represent the road, and the numbers are the points along the road. The r1, r2, r3,...
labels represent different rides.
This way of visualization helps in quick understanding of what each case is trying to cover or represent in the problem space.
Analyzing the different cases provides several key insights:
Minimum Constraints (Edge Case): No passengers mean zero profit, serving as a base case for the problem.
Single Best Route: When there’s only one ride, the choice is straightforward—pick that ride for maximum profit.
Multiple Routes, Single Best Answer: Not all rides are equally profitable; some offer better gains than others. Prioritization is needed.
Equal Profit from Different Rides: There could be rides that offer the same profit but cover different points. Here, the choice may affect subsequent possibilities, suggesting the need for an optimal selection strategy.
Gaps in Points with No Rides: If a segment of points offers no rides, there is no profit to be made there, and it should be skipped if possible.
Multiple Rides Starting from the Same Point: When multiple rides start from the same point, the choice can be critical, as some rides may offer better returns or set up for even better subsequent rides.
These insights hint that the problem is not just about maximizing immediate profit but about making strategic choices that result in an overall maximization of profit. Thus, a naive approach that simply picks the most immediately profitable ride won’t work in all scenarios. The solution needs a way to evaluate multiple rides in a holistic manner, possibly considering future opportunities as well.
Identification of Applicable Theoretical Concepts
Several mathematical and algorithmic concepts can be applied to this problem to make it more manageable:
Dynamic Programming: Given the nature of the problem, Dynamic Programming (DP) can be a useful tool to solve it efficiently. The goal is to maximize profit, and that can be approached recursively, considering previous best profits for earlier stages.
Sorting: Sorting the rides by their starting or ending points can simplify the process of finding the next most profitable ride.
Greedy Algorithms: While a greedy approach alone won’t provide the optimal solution, it can be combined with other methods to enhance performance.
Priority Queues: These can be used to quickly identify the next best ride in terms of profit among multiple available options.
Binary Search: After sorting the rides, binary search can be used to quickly find the next available ride, helping to improve the algorithm’s efficiency.
Memoization: Storing already calculated optimal profits for certain points can reduce repetitive calculations, thus saving time.
Graph Theory: You could also model this problem as a weighted directed graph where vertices are points on the road and edges are the rides, weighted by their profits. But the inherent complexity of keeping track of the best paths might make this approach less efficient than Dynamic Programming.
Time Complexity Analysis: Understanding the time complexity of different parts of the algorithm can help identify bottlenecks and make targeted improvements.
By understanding these concepts and applying them judiciously, the problem can be solved much more efficiently than by a naive bruteforce approach.
Simple Explanation
Imagine you’re a taxi driver. You’re on a road that has several stops, numbered from 1 to the last stop. You start at the first stop and want to make the most money by the time you reach the last stop. Along the way, people want to be picked up at certain stops and dropped off at others, and they’ll give you a tip for the ride.
Your goal is to figure out which people to pick up and drop off to make the most money. You can only take one person at a time and you can’t turn around; you can only move from the first stop to the last stop.
So, you have to make smart choices: which passengers will give you the most money for the least amount of driving? That’s the core problem you’re trying to solve.
Problem Breakdown and Solution Methodology
Mapping the Journey: Think of the road as a number line where each point is a potential pickup or dropoff spot for passengers. We’ll start at point 1 and have to get to point
n
.Evaluating Options: At each point, you have a choice to make. You can either pick up a new passenger if one is waiting or continue without picking anyone up. This is akin to standing at a crossroads and deciding which path to take.
Profit Calculation: For each passenger, you have to calculate how much money you’ll make if you choose them. This is like weighing the gold coins at the end of each path before deciding which one to take.
Optimization: Instead of going through all combinations of passengers—which would be extremely timeconsuming—you would use Dynamic Programming (DP) to keep track of the maximum profit at each point on the road. The DP technique is like having a map where you jot down the best earnings at each point so far, helping you to make better decisions down the line.
Decisionmaking: At each point, you decide whether picking up a new passenger will give you more money than continuing with your current passenger (if you have one). This decision will be based on the precalculated values from the DP step. It’s as if you have a financial advisor at each point telling you the best financial choice based on your ’earnings map'.
Journey’s End: When you reach point
n
, the value you have is the maximum amount of money you can make.
Effects of Changes in Problem Parameters:
 More Points (n): If there are more points on the road, the solution will still work, but it’ll take longer to compute.
 More Rides: Adding more rides will increase the choices you have, but it’ll also make the calculations more complex. DP would still be a good choice here.
Example:
Let’s say we have 5 points on the road and two ride options:
 Ride A from point 2 to 5 with a tip of 4 (Total money = 5  2 + 4 = 7)
 Ride B from point 1 to 5 with a tip of 1 (Total money = 5  1 + 1 = 5)
 Start at point 1: Best choice is to pick up Ride B (Potential earnings: 5)
 At point 2: Decide between continuing with Ride B (Potential earnings: 5) or switching to Ride A (Potential earnings: 7). Ride A is better.
 Continue to point 5 and drop off Ride A. Total earnings = 7.
You’d use a DP table to keep track of these calculations, ensuring you make the best choice at each point.
So, by breaking the problem down into smaller decisions and using DP to keep track of the best options, you can find the best way to make the most money by the end of your trip.
Inference of ProblemSolving Approach from the Problem Statement
Key Terms and Concepts
Points on the Road (n)
 These are the locations where you can pick up or drop off passengers.
 Strategy: Use these as states for your Dynamic Programming (DP) table.
Rides Array (starti, endi, tipi)
 Represents passengers waiting to be picked up, their destinations, and tips.
 Strategy: Sort this array based on the starting points or end points to efficiently find potential passengers at each location.
Optimal Profit
 The goal is to maximize the money earned through the journey.
 Strategy: Use optimization techniques like DP to find the highest profit at each point on the road.
Dynamic Programming (DP)
 A technique for solving optimization problems by breaking them down into smaller subproblems.
 Strategy: Use DP to keep track of the maximum profit at each point, which helps you decide whether or not to pick up a passenger.
Decisionmaking at Each Point
 At each point on the road, you have to decide whether to pick up a new passenger or continue with your current one (if any).
 Strategy: Use the DP table to inform these decisions, picking the option that offers the maximum future profit.
By identifying these key terms and concepts, we realize the problem boils down to making a series of decisions to maximize profit. We employ Dynamic Programming to keep track of these decisions efficiently, enabling us to solve the problem without having to explore every possible combination of rides.
Points on the Road (States):
 A linear diagram or table can be drawn to represent each point on the road (1, 2, 3, …, n).
Rides Array:
 Create a table with columns labeled ‘Start Point’, ‘End Point’, and ‘Tip’.
 Each row represents a passenger’s ride details.
 Alternatively, use arrows on the linear diagram to denote start and end points for each ride.
Optimal Profit (Dynamic Programming Table):
 Next to your linear diagram of points, add another column to represent the maximum profit that can be earned up to each point.
 Update this DP table as you proceed through the problem.
Decisionmaking:
 For each point in the linear diagram, use markers or notes to indicate which passenger you’d pick up for maximum profit.
 These can be pointers to rows in the Rides table.
Dynamic Programming Transitions:
 Draw arrows between states in your DP table to visualize how profit values propagate from one point to another based on your decisions.
By visualizing these elements, you can see the progression of maximum profit and decisions at each point, making it easier to understand how the solution is built up.
How did you infer from the problem statement that this problem can be solved using ?
Simple Explanation of the Proof
I’m having trouble understanding the proof of this algorithm. Could you explain it in a way that’s easy to understand?
Stepwise Refinement
Sorting and Initialization
 Sort the ‘rides’ array by the starting point of each ride.
 Initialize a Dynamic Programming (DP) array of size
n+1
with zeros to keep track of the maximum earnings.
Iterate Through Points
 Start iterating from point 1 to point n on the road. In each iteration, do the following:
Maximum Profit Without New Ride
 At each point, check if just driving to this point without picking any new passengers gives you more profit than picking one. Update the DP array accordingly.
Evaluate New Rides
 Evaluate all rides starting at the current point.
 For each ride, calculate the new possible profit (
endi  starti + tipi + DP[starti]
).  Update DP array if the new profit is better.
Update DP Array
 Use the calculated profits to update the DP array for future points (
endi
).
 Use the calculated profits to update the DP array for future points (
Final Answer
 Once the iteration is done, DP[n] will have the maximum profit you can make by the time you reach the end.
Granular, Actionable Steps
 Sort Rides: Sort the ‘rides’ array by their start points.
 Initialize DP Array: Create a DP array of zeros of size
n+1
.  Start Loop: Iterate through each point
i
on the road from 1 to n.  Check Previous Profit: Assign
DP[i] = DP[i1]
if not picking up a ride at this point gives more profit.  Nested Loop for Rides: Iterate through all rides starting at point
i
. Calculate Profit: For each ride, calculate potential profit.
 Update DP: If the new profit is higher, update
DP[endi]
with the new value.
 End Loops: End both the nested loop for rides and the main loop for road points.
Independent Parts
 The calculation of profit for each ride starting at the current point is independent of the others.
Repeatable Patterns
 The operation of iterating through each point and updating the DP array is a repeating pattern.
 Calculating the potential profit for each ride at a given point and updating the DP array is another repeating pattern.
By breaking the problem down this way, you can focus on solving each smaller problem one step at a time, eventually leading to the overall solution.
Solution Approach and Analysis
Initialization
 Create a Dynamic Programming (DP) array with length
n+1
, filled with zeros.  Sort the rides array by their starting point.
 Create a Dynamic Programming (DP) array with length
Iterate Through Road Points
 Start from the beginning of the road (
point 1
) and go to the end (point n
).
 Start from the beginning of the road (
Calculating Maximum Earnings Without New Ride
 At each point, the maximum earnings without picking a new passenger is the maximum earnings up to the previous point.
Find All Rides Starting at Current Point
 Look for rides that start at the current point.
Evaluate Profit for New Rides
 For each ride starting at the current point, calculate the potential profit by considering the route length and the tip.
Update DP Array
 Update the DP array for points where you can drop a passenger. If taking this ride leads to more profit, update the corresponding DP value.
Retrieve Maximum Earnings
 Once we reach the end (
point n
), the DP array’s last element will contain the maximum earnings.
 Once we reach the end (
Metaphor
Imagine you’re a treasure hunter in a linear tunnel with n
chambers. Each chamber might contain some treasure. You’re calculating the best strategy to collect the most treasure, considering each chamber has its treasure value and a ’tip’ for picking it up.
Specific Operations Affecting the Solution
Number of Rides (
rides.length
) The more rides available, the longer it will take to process them, affecting computational time.
Number of Points (
n
) More points lead to a larger DP array, impacting both memory and computational time.
Size of Tips (
tipi
) Larger tips can make shorter routes more profitable, affecting which rides you choose.
Example
Let’s consider Example 1: n = 5, rides = [[2, 5, 4], [1, 5, 1]]
Initialization: DP =
[0, 0, 0, 0, 0, 0]
, Sorted rides =[[1, 5, 1], [2, 5, 4]]
Point 1: Max earnings = 0. Available ride:
[1, 5, 1]
New earnings =4 (5  1 + 1)
, DP =[0, 0, 0, 0, 0, 4]
Point 2: Max earnings = 0. Available ride:
[2, 5, 4]
New earnings =7 (5  2 + 4)
, DP =[0, 0, 0, 0, 0, 7]
Point 3 to 5: No new rides. Maximum earnings =
7
Point 6: DP[5] = 7 (maximum earnings)
By following these steps, the maximum earnings are 7 dollars, which is the answer.
Identify Invariant
The invariant in this problem is the principle that the maximum earnings at any given point i
on the road are either:
 The maximum earnings that can be made up to the previous point
i1
, OR  The maximum earnings that can be made by completing a ride that ends at this point
i
.
This invariant holds true for each point i
on the road as you progress from point 1 to point n
. It provides a consistent rule that allows us to construct a DP array to find the maximum earnings efficiently.
Identify Loop Invariant
The loop invariant for this problem, assuming you’re using a dynamic programming approach, is that the DP array’s value at any index i
correctly represents the maximum earnings that can be made up to that point i
on the road.
For example, if you’re looping from 1 to n
to fill in the DP array, then before each iteration of the loop, the DP array values from 1 to i1
should already contain the maximum earnings that can be achieved up to those respective points. After the loop iteration for point i
, the DP array’s value at index i
should also represent the maximum earnings that can be made up to that point.
This invariant ensures that our dynamic programming solution is constructed correctly. It guides the loop through its computation and helps prove the correctness of the algorithm.
Thought Process
Understand the Objective: Recognize that we aim to maximize earnings by picking up passengers. The earnings are calculated as the distance covered plus the tip.
Identify Constraints: Observe that you can only pick one passenger at a time and that you can only move in one direction on the road.
Approach Identification: A Dynamic Programming (DP) approach is wellsuited for maximizing some value under given constraints. Each state in the DP array will represent the maximum earning up to that point.
Sort Rides: Sort the
rides
array by the end point. This will be useful to update the DP array more efficiently.DP Array Initialization: Create a DP array of length
n+1
, initialized to zeros, wheredp[i]
will represent the maximum earnings up to pointi
.Loop through Rides: Loop through the sorted
rides
array, and update the DP array according to the earnings possible by taking each ride.DP Transition: For each ride
[start, end, tip]
, transition would be likedp[end] = max(dp[end], dp[start] + (end  start) + tip)
.Result: The value at
dp[n]
will be our answer, i.e., the maximum earnings from point1
to pointn
.
Code
Python solution:


Key Insights
 The problem is essentially a DP problem disguised in the context of taxi rides. The DP array helps in tracking the maximum earnings at each point.
 Sorting the
rides
array based on end points helps in efficient DP transitions.  The DP transition takes into account whether to accept or skip a ride to maximize the earnings.
To implement the function maxTaxiEarnings
within the Solution
class in Python3, you’ll need to follow the thought process and steps outlined earlier. Here’s how to do it:


In this implementation, we import the List
type for type annotation. We follow the exact same steps as the previous explanation but encapsulate them within the maxTaxiEarnings
method of the Solution
class. The dp
array keeps track of the maximum earnings at each point on the road, and we update it based on the available rides. Finally, we return the maximum earnings possible from point 1 to point n, which is stored in dp[1]
.
Establishing Preconditions and Postconditions
Parameters:
 The inputs to the method are
n
andrides
. n
is of typeint
, andrides
is of typeList[List[int]]
.n
represents the number of points on the road from 1 to n.rides
is a 2D list, where each inner list has three integers: the start point, end point, and tip for a passenger ride.
 The inputs to the method are
Preconditions:
n
must be an integer greater than or equal to 1.rides
must be a list of lists, where each inner list contains exactly three integers. Each inner list must adhere to 1 <= start < end <= n and 1 <= tip <= 105.
 There is no specific state that the program or some part of it must be in before this method is called.
Method Functionality:
 The method is expected to return the maximum earnings a taxi driver can make by picking up and dropping off passengers optimally.
 It uses dynamic programming to keep track of the maximum earnings at each point on the road from 1 to n.
Postconditions:
 After the method returns, the program has computed the maximum earnings possible for the given parameters.
 The return value is an integer that represents the maximum earnings a taxi can make.
 There are no side effects.
Error Handling:
 The method doesn’t explicitly handle errors related to invalid input types or values.
 If preconditions are not met, Python will likely raise a type or index error, but no specific exception handling is built into the method.
Problem Decomposition
Problem Understanding:
 The problem is to find the maximum earnings a taxi driver can make by optimally choosing rides to pick and drop passengers. The key components are the number of points (
n
) on the road and a list of available rides (rides
). Each ride has a starting point, an ending point, and a tip amount. The requirements are to calculate maximum earnings considering both the fare (end point  start point) and the tip.
 The problem is to find the maximum earnings a taxi driver can make by optimally choosing rides to pick and drop passengers. The key components are the number of points (
Initial Breakdown:
 Major parts:
 Reading and understanding the input data (
n
andrides
).  Sorting the rides based on some criterion (like end points).
 Dynamic programming to keep track of maximum earnings at each point.
 Identifying the best rides to pick based on earnings.
 Reading and understanding the input data (
 Major parts:
Subproblem Refinement:
 Reading and Understanding Data:
 Parse
n
.  Parse
rides
.
 Parse
 Sorting Rides:
 Use sort function to sort by end point.
 Dynamic Programming:
 Initialize an array to keep track of earnings.
 Update the array based on best choices.
 Identifying Best Rides:
 Backtrack to find which rides were selected.
 Reading and Understanding Data:
Task Identification:
 Updating the dynamic programming array is a repeated task. It happens each time we evaluate a new ride.
Task Abstraction:
 Each task is abstracted enough to be clear and reusable. For instance, sorting the rides is a task that could be reused in a similar but different problem.
Method Naming:
 Reading and Understanding Data:
parseInput
 Sorting Rides:
sortRides
 Dynamic Programming:
calculateEarnings
 Identifying Best Rides:
identifyBestRides
 Reading and Understanding Data:
Subproblem Interactions:
parseInput
needs to be performed first to getn
andrides
.sortRides
should be done after parsing and before dynamic programming.calculateEarnings
relies on sortedrides
and produces data needed foridentifyBestRides
. There is a dependency chain:
parseInput
>sortRides
>calculateEarnings
>identifyBestRides
.
From Brute Force to Optimal Solution
Brute Force Solution
Overview
A brute force solution would involve evaluating all possible combinations of rides the taxi could take and then selecting the combination with the highest total earnings. We would start at the first point and explore every possible sequence of rides, calculating the earnings for each one.
Steps
 Start from point 0 and recursively explore all possible combinations of the next rides.
 At each step, decide to either take or skip the current ride.
 Accumulate the earnings and return the maximum earning out of all possible combinations.
Inefficiencies
 Time Complexity: O(2^n), where n is the number of rides. This is because for each ride, you either take it or don’t, making it exponentially expensive.
 Space Complexity: O(n), mainly for the recursion stack.
Optimization
Step 1: Sort the Rides
Sorting the rides by their ending point will make it easier to manage the problem. This way, we can easily find rides that are available after each ride ends.
 Impact: Sorting takes O(n log n) time but it simplifies subsequent steps.
Step 2: Dynamic Programming
Instead of evaluating each combination from scratch, we can use dynamic programming to store the maximum earnings possible up to each end point. This will save us from redundant calculations.
 Create an array,
dp
, of length equal to the maximum point (n
) and initializedp[0] = 0
.  For each ride ending at point
i
, calculatedp[i] = max(dp[i], dp[start] + earnings)
. Here,start
is the starting point of the ride andearnings
is the total fare plus tip for that ride.
 Impact: Reduces time complexity to O(n log n) due to sorting and linear scan.
Step 3: Binary Search
While updating dp[i]
, we need to find the maximum earnings that can be obtained before the starting point of the current ride. We can use binary search to find this quickly.
 Impact: Binary search in a sorted list takes O(log n), which keeps our time complexity at O(n log n) but makes the constant factors lower.
Step 4: Use Iterative DP
Instead of recursive dynamic programming, use iterative dynamic programming to save space.
 Impact: This will reduce the space complexity from O(n) to O(1) apart from the input and
dp
array, as we won’t be using a recursion stack.
Final Time and Space Complexity
 Time Complexity: O(n log n) due to sorting and binary search.
 Space Complexity: O(n) for the dynamic programming array.
By adopting these optimizations, we dramatically reduce the time and space requirements compared to the bruteforce approach, making the problem much more manageable.
Code Explanation and Design Decisions
1. Initial Parameters
n
: This is the total number of endpoints in the city. It sets the boundary for our problem domain.rides
: A list of rides, where each ride is represented as[start, end, tip]
. It is the primary data that we’re trying to optimize.
2. Primary Loop
The primary loop iterates over each endpoint from 0 to n
. Each iteration represents a state: the maximum earnings a taxi can make when it ends its last ride at that endpoint. The DP array is updated at each endpoint to reflect the best earnings.
3. Conditions Within Loop
Inside the loop, there’s a condition to compare dp[i]
with dp[start] + earnings
for each ride that ends at i
. This condition ensures we store the maximum earnings possible up to this endpoint, considering whether or not to take a particular ride.
4. Updates to Parameters
We update the dp
array. Specifically, dp[i]
is updated to be the maximum of its current value and dp[start] + earnings
. This change is essential because it maintains the invariant that dp[i]
always contains the maximum earnings possible up to endpoint i
.
5. Invariant
The invariant here is the dp
array. It maintains the maximum possible earnings for each endpoint, assuming optimal choices are made. It helps meet the objective of finding the most earnings possible for the taxi.
6. Final Output
The final output is dp[n]
, which represents the maximum earnings possible for the taxi when it has considered all rides. This satisfies the problem requirement by providing the optimized earnings for the given set of rides.
The focus is on incrementally building the best earnings state, allowing us to efficiently find the optimal solution.
Coding Constructs
1. HighLevel Strategies
The code uses Dynamic Programming to optimize taxi earnings. It constructs a “memory” (dp
array) to keep track of the maximum earnings up to each endpoint.
2. NonProgrammer Explanation
Imagine you’re a taxi driver deciding which rides to take to make the most money, including tips. This program helps you decide the best sequence of rides to maximize your earnings by the end of the day.
3. Logical Elements
 Iteration: Looping over all endpoints.
 Conditionals: Checking for better earnings possibilities.
 State Updating: Keeping track of best earnings up to each endpoint.
4. Algorithmic Approach in Plain English
Start with an empty pocket. For each location in the city, check all the rides ending at that location. Decide whether taking each ride will increase your earnings. Always update your pocket with the most money you could have when you reach that location. Do this until you reach the last location.
5. Key Steps on Input Data
 Iterates over all endpoints in the city (
n
).  For each endpoint, reviews all the rides ending at that endpoint.
 Compares potential earnings and updates the maximum earnings achievable up to that endpoint.
6. Algorithmic Patterns
 Dynamic Programming: Used for optimizing an objective function (earnings in this case).
 Looping: To iterate over endpoints and rides.
 Conditionals: To make earningsbased decisions.
The code is designed to find the most optimized earnings by exploring all possibilities in an efficient manner. It does so by breaking down the problem into smaller, solvable parts and building up the solution.
Language Agnostic Coding Drills
1. Distinct Coding Concepts
 Variable Initialization: Storing basic data types.
 Arrays and Lists: Managing collections of data.
 Looping Constructs: Forloops, whileloops, etc.
 Conditional Statements: Using
if
,else
constructs.  Sorting a List of Lists: Sorting complex data types based on some criterion.
 Array Indexing and Manipulation: Accessing and modifying data within arrays/lists.
 Dynamic Programming: Using a memory or cache to store subproblem solutions.
 Function Definition: Creating reusable blocks of code.
2. Concepts in Increasing Difficulty
 Variable Initialization: The simplest task, just storing values.
 Arrays and Lists: A step above variables, holding multiple values.
 Looping Constructs: Requires understanding of flow control.
 Conditional Statements: Adds decisionmaking to the flow control.
 Array Indexing and Manipulation: Requires list access and update operations.
 Sorting a List of Lists: Complexity arises from dealing with sublists.
 Function Definition: Abstracts logic into a reusable component.
 Dynamic Programming: Most complex, needs an understanding of subproblems and optimization.
3. ProblemSolving Approach
Variable Initialization: Start by initializing variables to store the maximum earnings at each dropoff point.
 Contribution: Sets the stage for dynamic programming.
Arrays and Lists: Create a list to hold the ride information.
 Contribution: Makes ride information readily available.
Sorting a List of Lists: Sort the list of rides based on the endpoint.
 Contribution: Simplifies the problem by making it easier to find rides that end at the same point.
Looping Constructs: Iterate through the list of sorted rides.
 Contribution: Allows us to process each ride one by one.
Array Indexing and Manipulation: Access and update the maximum earnings at each point using array indexing.
 Contribution: Makes it efficient to update and retrieve earnings at any point.
Conditional Statements: Within the loop, use conditionals to decide whether taking a ride increases the maximum earnings.
 Contribution: Enables the algorithm to make choices that optimize earnings.
Function Definition: (If applicable) Use functions to abstract out repeated logic.
 Contribution: Makes the code cleaner and possibly reusable.
Dynamic Programming: Integrate dynamic programming to store the maximum earnings at each dropoff point in a list or array. Use this stored information to avoid redundant calculations.
 Contribution: Brings it all together. Makes the solution efficient by preventing redundant work and storing results of subproblems.
This way, each ‘drill’ or concept contributes to solving a part of the larger problem. When you assemble them, you get a complete, optimized solution.
Targeted Drills in Python
1. Coding Drills for General Concepts
Variable Initialization


Arrays and Lists


Looping Constructs


Conditional Statements


Sorting a List of Lists


Array Indexing and Manipulation


Function Definition


Dynamic Programming


2. ProblemSpecific Drills
Finding Maximum in List
This concept is crucial for finding the maximum earnings at each point.


3. Integrating the Drills
Initialize Variables: Start by initializing an array to keep track of maximum earnings at each point.
Create List of Rides: Use the array/list concept to initialize a list with ride information.
Sort List of Rides: Sort the list using the “Sorting a List of Lists” drill.
Forloop Iteration: Use looping constructs to iterate through the list of sorted rides.
Conditional Statements for Earnings: Inside the loop, use conditional statements to check if taking a specific ride would maximize earnings.
Update Earnings: If yes, update the maximum earnings using array indexing and manipulation.
Use Functions: Wrap repeated logic inside functions to make the code clean and reusable.
Dynamic Programming: Finally, integrate the dynamic programming approach to keep track of the maximum earnings efficiently. Utilize the memoization concept here.
Find Max Earnings: At the end of all iterations, find the maximum earning among all points using the “Finding Maximum in List” drill.
The drills can be combined in the above sequence to form a complete, efficient solution to the problem.
Q&A
Similar Problems
Here are 10 problems that share underlying concepts with the problem we’ve discussed:
Coin Change (LeetCode #322)
 Why: Utilizes dynamic programming to find the minimum number of coins to make a target amount, similar to finding maximum taxi earnings.
Longest Increasing Subsequence (LeetCode #300)
 Why: Also uses dynamic programming to find an optimal subsequence, similar to how we find optimal earnings.
01 Knapsack (LeetCode as a common pattern)
 Why: A classic optimization problem using dynamic programming to maximize value, closely related to maximizing taxi earnings.
Jump Game II (LeetCode #45)
 Why: Requires finding the minimum number of jumps to reach the end of an array. Relates to our problem through optimization and array manipulation.
Best Time to Buy and Sell Stock (LeetCode #121)
 Why: Similar to finding the maximum taxi earnings, here we are maximizing the profit.
Partition Equal Subset Sum (LeetCode #416)
 Why: Another problem that deals with finding optimal subsets, which is similar to finding a set of rides for maximum earnings.
House Robber II (LeetCode #213)
 Why: Utilizes dynamic programming to find the maximum amount you can rob from houses. Shares the idea of optimizing earnings or loot.
Climbing Stairs (LeetCode #70)
 Why: A simpler dynamic programming problem that can serve as a stepping stone for understanding more complex problems like our taxi problem.
Unique Paths (LeetCode #62)
 Why: Dynamic programming is used to find all unique paths in a grid, sharing optimization techniques.
Minimum Path Sum (LeetCode #64)
 Why: This problem also involves navigating through a grid to minimize a sum, which is the inverse of our objective of maximizing earnings. Uses similar dynamic programming techniques.
These problems require similar strategic thinking, mostly revolving around dynamic programming, optimization, and array manipulation.