The Problem:

In this problem, we are given a linked list and a value, our goal is to remove all the nodes that have a node of greater value to their right.

Before we begin trying to figure out a solution for this problem, let’s first revisit an important concepts Linked List and Recursion.

Understanding Recursion:

Recursion is essentially a function calling itself. It is frequently used to solve problems that can be divided into smaller, similar sub-problems and can be very useful in dynamic programming. To understand recursion, it can be helpful to break it down into 3 parts:

1. Base Condition: This condition prevents the recursive function from calling itself infinitely.
2. Recursive Call: This is responsible for calling the recursive function itself.
3. Small Calculation: This step, which can come before or after the Recursive Call, is responsible for performing some calculation that we need for the recursive function or for returning some result after a recursive call has been completed.

A recursive tree is a visual representation of the recursive calls made by a function. It can be very helpful in understanding how recursion works and in analyzing the time and space complexity of recursive algorithms.

Understanding Linked List:

A linked list is a linear data structure that consists of a sequence of nodes, each containing data and a reference to the next node in the list. It is often used as an alternative to arrays, as it can be more efficient in certain operations such as insertion and deletion.

To understand linked lists, it can be helpful to break it down into 3 parts:

1. Nodes: Each node in a linked list contains some data and a reference to the next node in the list.
2. Head: The head of a linked list is the first node in the list. It is used as the starting point for many operations on the list.
3. Traversal: Traversing a linked list involves starting at the head and following the references from one node to the next until the desired node is reached or the end of the list is reached.

Linked lists can be very useful in solving problems that involve dynamic data structures, as they allow for efficient insertion and deletion of elements. They can also be used in combination with other data structures, such as stacks and queues, to solve more complex problems.

Intuition:

With an understanding of how recursion and linked lists work, let’s try to break down the problem. We don’t have to worry about making our own linked list data structure as it is given. This is a fairly straightforward problem, so we will begin by figuring out the base condition. Which in this case would be when the recursion reaches the end of the linked list or when the head value is None.

The recursive call can be made for the next node to the current node and when we reach the last node, the recursion will start returning. We will perform the small calculation here, which would be to check if the current node value is less the next node value, if it is then return the next node, else we return the current node.

This ensures that nodes with a greater value to their right are removed from the list.

Summary of Solution

Use recursion to remove nodes from linked list if the current node’s value is less than the next node’s value. Update the next pointer of each node to point to the result of recursively calling removeNodes on the rest of the list. Return the new head of the list.

Code

This can be translated to code in the following way:

# Definition for singly-linked list.
# class ListNode:
#     def __init__(self, val=0, next=None):
#         self.val = val
#         self.next = next
class Solution:
    def removeNodes(self, head: Optional[ListNode]) -> Optional[ListNode]:
        if head == None: # base
            return head
        head.next = self.removeNodes(head.next) # recursive call
        if head.next and head.val < head.next.val: # calculation
            return head.next
        return head

• Time Complexity O(n)
• Space Complexity O(n)

The time complexity of this solution is O(n) This is because the function needs to traverse the linked lists, and the time it takes to do so is proportional to the length of the linked list.

This approach is simple and efficient, making it a good solution to this problem.

I hope you enjoyed this post. Stay tuned for daily LeetCode blog posts!