Next Greater Element I
Identifying Problem Isomorphism
“Next Greater Element I” has an approximate isomorphism: “Next Greater Node In Linked List” (#1019).
In “Next Greater Element I”, you’re given two arrays, nums1 and nums2, and the task is to find the next greater number for each element in nums1 in the corresponding places of nums2. If it does not exist, output 1 for this number.
In “Next Greater Node In Linked List”, you’re given a linked list and for each node, you need to find the next node in the list which has a larger value.
Both have a similar structure in that they both require you to find the ’next greater’ value for each element in a collection. This can be done using a similar approach, typically using a stack to keep track of the elements for which you’re still looking for a ’next greater’ value.
As you iterate through the array or linked list, you can pop elements from the stack until you find one that is greater than the current element, and then push the current element to the stack. At the end, any elements still in the stack don’t have a ’next greater’ value.
This is an approximate isomorphism. While the core logic of finding the next greater value is similar, the exact problem constraints and setup are different  one operates on arrays while the other operates on a linked list, and the ’next greater’ value in the second problem is the value of the next node rather than the next element at a corresponding index. These differences mean that you will have to adjust the implementation details to fit the specifics of each problem.
To solve this problem, we will use a data structure called a stack, and a dictionary (or hashmap) to store the mapping of an element to its next greater element in nums2
.
Here is a step by step approach:
 Traverse the
nums2
array from left to right.  While the stack is not empty and the top of the stack is less than the current element, pop the stack and add an entry in the map where the key is the popped element and the value is the current element.
 Push the current element onto the stack.
 Repeat steps 2 and 3 until all elements in
nums2
are traversed.  After the above steps, we have a map with each element of
nums2
and its next greater element (if it exists).  Now, we traverse the
nums1
array and for each element, we look it up in the map. If it is found, we add the corresponding value in the result, otherwise, we add1
.
The idea is to maintain a stack of elements for which we have not yet found the next greater elements. For every new number that we are considering (from nums2
), we check whether it is greater than the last number added to the stack. If it is, that means we have found the next greater number, so we add it to the map and remove the number from the stack.
Here is the code:


Here map.get(num, 1)
will return 1
if num
is not found in the map. This represents that there is no next greater element for the given number in nums2
.
In this way, the code effectively solves the problem using a stack and a map, fulfilling the constraints given in the problem.
This is a great example of how stacks can be used in conjunction with hashmaps to efficiently solve problems related to the ordering and positioning of elements in an array.
The stack is used to find the next greater element in the nums2
array. The stack is maintained in a monotonically decreasing order  the top of the stack always contains the smallest element for which we are yet to find the next greater element.
Here’s how it works:
 We initialize an empty stack and start traversing the elements of
nums2
.  For each element:
 We keep comparing it with the top of the stack.
 If the current element is greater than the top of the stack, we know that we’ve found the next greater element for the stack’s top element. We pop the element from the stack and add an entry to our map with the popped element as the key and the current element as the value.
 This process is repeated until we either empty the stack or find an element in the stack which is greater than the current element.
 After the comparisons, we push the current element onto the stack.
 We continue this process for each element in
nums2
.
By the end of this process, the stack contains the elements for which no greater element exists in nums2
, and our map contains the next greater element for every other element in nums2
. We use this map to answer the queries for elements in nums1
.
Monotonous Stack
This is a perfect example of a monotonous stack.
A monotonous stack is a stack that is either entirely increasing or entirely decreasing, depending on whether you are looking for the next greater or smaller elements respectively. In this problem, we’re dealing with a monotonically decreasing stack to find the next greater element.
The process works like this:
 Initialize an empty stack.
 Iterate over the elements of the array
nums2
.  While the stack is not empty and the top element of the stack is less than the current element, pop elements from the stack. For each popped element, the current element is the next greater element.
 Push the current element onto the stack.
The use of a monotonously decreasing stack in this problem ensures that, at any point, the stack contains elements that are yet to find their next greater elements, with the top of the stack being the smallest among them.
This is a powerful technique used to solve many problems related to finding next larger or smaller elements, and it can significantly optimize the performance from a naive or bruteforce approach.
Basic Building Blocks
The solution for this problem primarily consists of two building blocks:
Preprocessing Data: We are preprocessing the data in
nums2
by creating a mapping of each element to its next greater element. This mapping is stored in a hashmap, which allows for quick lookups later when we are processingnums1
.Monotonous Stack: The monotonous stack is a crucial building block here. It’s used to keep track of elements in
nums2
for which we are yet to find the next greater element. As we iterate throughnums2
, for each number, we keep popping the stack while the top of the stack is less than the current number, and for every popped number, we add an entry to the hashmap. Then we push the current number onto the stack.
These are the main building blocks in the solution. There isn’t another distinct building block as such, but it’s important to note that the process of iterating over nums1
and looking up the next greater element from the hashmap (which was generated during preprocessing) is essentially a process of data retrieval. This could be considered as a building block as well.
However, data retrieval is so fundamental to many algorithms that it might not typically be singled out as a separate building block in the same way as preprocessing or using a monotonous stack. In this case, the use of preprocessing and a monotonous stack is the key to efficiently solving this problem.
Brute Force
The brute force approach to this problem would be to simply use two nested loops to solve it. For each number in nums1
, traverse nums2
from the index of that number until the end of nums2
, looking for the next greater element.
Here is a stepbystep breakdown:
 For each element in
nums1
, find its index innums2
.  Starting from that index, traverse
nums2
until you find an element greater than the current element ofnums1
. This will be the next greater element.  If no greater element is found by the end of
nums2
, set the next greater element as 1.  Repeat steps 13 for each element in
nums1
.
Here’s how that looks in Python code:


This brute force solution is not very efficient. It has a time complexity of O(n^2) in the worst case, where n is the length of nums2
.
To improve this, we use a stack and a hashmap as described earlier. The stack helps us keep track of the elements for which we have not yet found the next greater elements. The hashmap allows us to quickly lookup the next greater element for any given number.
While iterating over nums2
, for each number, we keep popping the stack while the top of the stack is less than the current number, and for every popped number, we add an entry to the hashmap. Then we push the current number onto the stack.
After this, the hashmap contains the next greater element for every number in nums2
. We then iterate over nums1
and find the next greater elements from the hashmap.
This optimized approach reduces the time complexity to O(n), which is a significant improvement over the brute force solution.


Alternative Solution : Implicit Stack
It’s possible to solve this problem without explicitly using a stack. Instead, we can scan nums2
in reverse, maintaining a map to keep track of the next greater element for each number, which essentially gives us the effect of a monotonous stack.
In this approach, the “stack” is implicit in the process of scanning the array backwards and maintaining the next greater element.
Here is how this approach looks in Python:


It traverses the nums2
array in reverse order, maintaining a mapping of each element to its next greater element. Then, it uses this mapping to create the result
array, which contains the next greater element for each element in nums1
.
This is a clever use of a hashmap and it does indeed provide an equivalent effect to using a monotonous stack. It’s a good example of how a problem can be approached in multiple ways. The choice between these approaches depends on the specific requirements and constraints of the problem at hand.
No, it doesn’t have the same effect as a stack. Stack guarantees m+n while the time complexity of this solution is n^n, which is even worse than bruteforce.
You’re correct, my apologies for the oversight. The while loop in the translated code does resemble a nested loop due to the map.get(k)
lookup inside the while condition. This could potentially lead to a time complexity of O(n^2), which is worse than the O(m+n) time complexity we achieve using a stack.
The while
loop is effectively searching for the next greater element, which, in a worstcase scenario, could traverse the entire nums2
list for each element, hence the O(n^2) complexity.
Utilizing a monotonous stack as discussed earlier is indeed more efficient as it guarantees each element is processed only twice (once for pushing onto the stack, and once for popping from the stack), thus maintaining an overall time complexity of O(m+n).
Thank you for pointing out the error, and I appreciate your patience in this matter. It’s essential to consider the time complexity when comparing different approaches to a problem.
Problem Classification
Domain Categorization: This problem falls within the domain of Data Structures and Algorithms, specifically dealing with arrays and element searching.
‘What’ Components:
 You have two integer arrays, nums1 and nums2, where nums1 is a subset of nums2.
 For each element in nums1, you need to find its index in nums2.
 Then find the next greater element to the right of that indexed element in nums2.
 If there is no next greater element, 1 should be returned for that particular element.
 The result should be an array of the same length as nums1, containing the next greater element for each corresponding element in nums1.
Problem Classification: This problem can be further classified as a “Next Greater Element” problem, a commonly seen problem pattern in competitive programming. It requires understanding of array manipulation, element comparison, and appropriate use of data structures for efficient lookup.
Explanation of Categorizations: The problem is about identifying and finding elements based on certain conditions (finding the next greater element) in an array, which is why it belongs to the Data Structures and Algorithms domain. The problem is specific to arrays and element comparison. It requires creating a new array based on the results, hence it’s a transformation type problem. The “Next Greater Element” categorization is based on the specific task required in the problem.
Language Agnostic Coding Drills
Dissection & Identification of Concepts: Here are the distinct coding concepts contained in this piece of code:
 Data Structures: The use of lists (arrays) to hold elements, dictionaries (hashmaps) for fast lookups and a stack for maintaining an order of elements.
 Looping Constructs: The usage of ‘for’ and ‘while’ loops to iterate over elements in the array.
 Conditional Statements: Use of ‘if’ conditionals to control the flow of the program.
 Array Indexing: Accessing elements in an array via indices.
 Stack Operations: The use of ‘pop’ and ‘append’ operations which respectively correspond to stack’s ‘pop’ and ‘push’ operations.
 Dictionary Operations: Storing and retrieving values in a dictionary.
Drills in Ascending Order of Difficulty:
 Array Indexing and Looping Constructs (Easy): Understanding how to access elements in an array and iterating over them using loops.
 Conditional Statements (Easy): Implementing ‘if’ conditions to decide when to perform certain operations.
 Basic Data Structures (Medium): Understanding and using lists (arrays) and dictionaries (hashmaps). This includes knowledge of when to use one over the other and why.
 Dictionary Operations (Medium): Adding entries to a dictionary and retrieving values from it.
 Stack Operations (Hard): Understanding the concept of a stack, how it operates (LIFO  Last In First Out), and how to use list operations to simulate a stack.
Problemsolving Approach & Steps:
 Step 1: Initialize an empty stack and a dictionary. The stack is used to maintain the elements for which we have not found a next greater element. The dictionary is used to store the next greater element for each element in
nums2
.  Step 2: Iterate over
nums2
and for each element, while the stack is not empty and the top of the stack is less than the current element, pop the top of the stack and add an entry to the dictionary with the key as the popped element and the value as the current element.  Step 3: After the loop, if there are still elements in the stack, it means there are no greater elements to their right. Add an entry to the dictionary for each of them with a value of 1.
 Step 4: Iterate over
nums1
and for each element, find its next greater element from the dictionary and append it to the result.  Step 5: Finally, return the result.
Each of these steps aligns with one or more of the identified coding concepts (or drills). The stepbystep problemsolving approach involves a combination of all these concepts to find the next greater element for each element in
nums1
. The steps have to be executed in the given order to get the correct result. Step 1: Initialize an empty stack and a dictionary. The stack is used to maintain the elements for which we have not found a next greater element. The dictionary is used to store the next greater element for each element in
Targeted Drills in Python
Python Drills for Each Concept:
 Array Indexing and Looping Constructs:
1 2 3 4 5 6
# Create a list nums = [1, 2, 3, 4, 5] # Use indexing to access the elements for i in range(len(nums)): print(nums[i])
 Conditional Statements:
1 2 3 4 5 6 7 8
# Check if a number is positive, negative or zero number = 5 if number > 0: print("Positive") elif number < 0: print("Negative") else: print("Zero")
 Basic Data Structures:
1 2 3
# Create a list and a dictionary students = ['Alice', 'Bob', 'Charlie'] grades = {'Alice': 'A', 'Bob': 'B', 'Charlie': 'C'}
 Dictionary Operations:
1 2 3 4 5 6
# Store count of each character in a string in a dictionary s = "Hello" char_count = {} for char in s: char_count[char] = char_count.get(char, 0) + 1 print(char_count)
 Stack Operations:
1 2 3 4 5 6
# Create a stack using a list and implement 'push' and 'pop' operations stack = [] stack.append('a') # push 'a' stack.append('b') # push 'b' top = stack.pop() # pop print(top) # 'b'
Additional Drills for ProblemSpecific Needs:
 Using Stack for Tracking Elements:
1 2 3 4 5 6 7 8 9
# Use a stack to keep track of elements in an array until a certain condition is met nums = [5, 4, 3, 2, 1] stack = [] for num in nums: while stack and stack[1] > num: print(stack.pop()) stack.append(num) while stack: print(stack.pop())
 Mapping Elements to Their Corresponding Values:
1 2 3 4 5
# Map elements in one array to corresponding elements in another array students = ['Alice', 'Bob', 'Charlie'] grades = ['A', 'B', 'C'] grade_map = dict(zip(students, grades)) print(grade_map)
Merging Drills for the Final Solution:
 Initialize an empty dictionary and an empty stack.
 Iterate over the elements in the
nums2
array (Array Indexing and Looping Constructs drill).  Check if the stack is not empty and the current element is greater than the top of the stack (Conditional Statements and Stack Operations drills).
 If the condition is met, pop the top element from the stack and add an entry to the dictionary with the popped element as the key and the current array element as the value (Stack Operations and Dictionary Operations drills).
 After checking the condition, push the current array element to the stack (Stack Operations drill).
 After the loop, add an entry to the dictionary for each element in the stack with a value of 1 (Dictionary Operations drill).
 Finally, iterate over the
nums1
array, retrieve the next greater element for each element from the dictionary and append it to the result list (Array Indexing, Looping Constructs, and Dictionary Operations drills).