Longest Substring Without Repeating Characters - Leetcode 3 - Python
NeetCode
6 min, 46 sec
The video provides an in-depth explanation of solving the LeetCode problem to find the length of the longest substring without repeating characters using a sliding window technique.
Summary
- The problem is to find the length of the longest substring without repeating characters in a given string.
- A brute force method would check every substring for duplicates, which is inefficient due to repeated work.
- The sliding window technique is introduced to eliminate the need to check substrings that are guaranteed to contain duplicates.
- A set is used to instantly check for duplicates as the window slides through the string, updating the longest length found.
- The algorithm has a time complexity of O(n) and uses a set for constant-time checks, resulting in a space complexity of O(n).
Chapter 1
Introduction to the classic LeetCode problem of finding the longest substring without repeating characters.
- The problem is presented as a common and simple one, where the goal is to find the longest substring without duplicate characters within a given string.
- An initial brute force approach is considered, where every substring is checked for duplicates, and the length of the longest unique substring is returned.
Chapter 2
The speaker considers a brute force method to solve the problem by checking all substrings for duplicates.
- The approach involves starting with the first character and generating all possible substrings, checking each for duplicates.
- This method includes a lot of repeated work, as it does not stop checking for substrings even after a duplicate occurrence is found.
Chapter 3
Introduction of the sliding window technique to optimize the solution and eliminate repeated work.
- The speaker suggests using a sliding window technique to maintain a substring that always excludes duplicates.
- As duplicates are found, the window is reduced from the left side until all duplicates are eliminated.
- A set is used to keep track of the characters in the current window, allowing for constant-time checks for duplicates.
Chapter 4
The implementation of the sliding window technique is explained with an illustration of removing duplicates.
- The sliding window is shifted each time a duplicate character is encountered, and the duplicate character is removed from both the window and the set.
- The process continues by adding new characters to the window and set and removing duplicates as necessary, all the while updating the longest substring length.
Chapter 5
The speaker finalizes the sliding window solution and discusses the time and space complexity of the algorithm.
- A result variable is introduced to keep track of the longest substring length found.
- Two pointers, left and right, are used to represent the sliding window, with the right pointer iterating through each character.
- The set is updated as the window changes, and the result is potentially updated with each new valid window size.
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