How do you solve Longest Substring Without Repeating Characters on LeetCode?

Longest Substring Without Repeating Characters (LeetCode #3) can be solved using Brute Force or Optimal Solution (Hash Map). The optimal approach is Optimal Solution (Hash Map) with O(n) time complexity and O(n) space complexity. Key insight: The sliding window technique is essential for optimizing problems involving substrings, as it allows for efficient expansion and contraction of the search space.

What is the time complexity of Longest Substring Without Repeating Characters?

The optimal solution for Longest Substring Without Repeating Characters runs in O(n) time and uses O(n) space. The time complexity is O(n) because we traverse the string once. The space complexity is O(n) due to the hash map storing the last index of each character.

What is the brute force solution for Longest Substring Without Repeating Characters?

The brute force approach for Longest Substring Without Repeating Characters is Brute Force, with O(n²) time complexity and O(1) space complexity. The brute force approach involves generating all possible substrings of the input string and checking each one for duplicates. This is straightforward but inefficient as it checks every possible combination. The optimal Optimal Solution (Hash Map) approach improves this to O(n).

What algorithm or data structure is used to solve Longest Substring Without Repeating Characters?

Longest Substring Without Repeating Characters uses the following concepts: Hash Table, String, Sliding Window. The recommended patterns to study are: Hash Map, Array, Two Pointers.

What are the interview tips for Longest Substring Without Repeating Characters?

When you first see this problem, mention that you will consider both brute force and optimal solutions, explaining your thought process. Communicate your approach clearly, discussing how you will handle duplicates and track the longest substring as you iterate through the string. Mention edge cases like an empty string or a string with all unique characters, and how your solution will handle them.

What are common mistakes when solving Longest Substring Without Repeating Characters?

Students often forget to update the left pointer correctly when encountering a duplicate character, which can lead to incorrect substring lengths. Another common mistake is not handling edge cases, such as empty strings or strings with all unique characters.

Longest Substring Without Repeating Characters

Medium

Hash TableStringSliding WindowHash MapArrayTwo Pointers

LeetCode ↗

Approaches

Brute ForceOptimal

Complexity Comparison

	Brute Force	Optimal Solution (Hash Map)★
Time	O(n²)	O(n)
Space	O(1)	O(n)

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Intuition

Time O(n)Space O(n)

The optimal solution uses a sliding window approach with a hash map to track the last seen index of each character. This allows us to efficiently expand and contract the window to find the longest substring without duplicates.

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Algorithm

4 steps

1Step 1: Initialize a hash map to store the last index of each character and two pointers for the sliding window.
2Step 2: Iterate through the string with one pointer, expanding the window.
3Step 3: If a character is found that is already in the hash map and is within the current window, move the left pointer to the right of the last seen index of that character.
4Step 4: Update the maximum length of the substring found.

solution.py15 lines

1# Full working Python code with comments
2
3def longest_substring_hash_map(s):
4    char_index = {}
5    max_length = 0
6    left = 0
7    for right in range(len(s)):
8        if s[right] in char_index:
9            left = max(left, char_index[s[right]] + 1)
10        char_index[s[right]] = right
11        max_length = max(max_length, right - left + 1)
12    return max_length
13
14# Example usage
15print(longest_substring_hash_map('abcabcbb'))  # Output: 3

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Complexity note: The time complexity is O(n) because we traverse the string once. The space complexity is O(n) due to the hash map storing the last index of each character.

1The sliding window technique is essential for optimizing problems involving substrings, as it allows for efficient expansion and contraction of the search space.
2Using a hash map to track character indices helps avoid unnecessary checks and allows for quick updates to the substring boundaries.

Solutions and explanations are original Tejav content. Problem titles © LeetCode — use the LeetCode button above for the full problem statement.