longest substring without repeating characters

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04-04-2025

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Finding the longest substring without repeating characters is a classic computer science problem that tests your understanding of string manipulation and algorithm design. This article explores this problem, drawing on insightful solutions from Stack Overflow, and adding further explanation and practical examples to enhance your understanding.

Understanding the Problem

The goal is simple: given a string, find the longest substring that contains no repeating characters. For instance:

• Input: "abcabcbb"

• Output: "abc" (length 3)

• Input: "bbbbb"

• Output: "b" (length 1)

• Input: "pwwkew"

• Output: "wke" (length 3)

Solutions from Stack Overflow and Deeper Analysis

Many efficient solutions exist, often employing a sliding window technique. Let's analyze a popular approach from Stack Overflow (though attributing specific users is difficult without the original question links, as the problem is frequently asked):

Sliding Window Approach

This method uses a sliding window to track the currently examined substring. A dictionary (or hash map) stores the characters within the window and their last seen indices. As the window slides, we update the dictionary and adjust the window's start index if a repeating character is encountered.

Python Code (Illustrative Example):

def longest_substring_without_repeating_characters(s):
    """
    Finds the longest substring without repeating characters using a sliding window.

    Args:
        s: The input string.

    Returns:
        The longest substring without repeating characters.
    """
    n = len(s)
    ans = ""
    # mp stores the current index of a character
    mp = {}

    i = 0
    j = 0
    while (i < n and j < n):
        if s[j] not in mp:
            mp[s[j]] = j
            if len(ans) < (j - i + 1):
                ans = s[i:j + 1]
            j += 1
        else:
            del mp[s[i]]
            i += 1

    return ans

#Example usage
string = "abcabcbb"
result = longest_substring_without_repeating_characters(string)
print(f"The longest substring without repeating characters in '{string}' is: {result}") #Output: abc

string = "pwwkew"
result = longest_substring_without_repeating_characters(string)
print(f"The longest substring without repeating characters in '{string}' is: {result}") #Output: wke

Explanation:

1. Initialization: We start with an empty ans string to store the longest substring found so far and an empty dictionary mp to track character indices. i and j represent the start and end of the sliding window, respectively.

2. Sliding Window: The while loop iterates through the string. If a character is not in mp (meaning it's not in the current window), we add it to mp, update ans if necessary, and move the window's end (j).

3. Repeating Character: If a character is already in mp, it means we've encountered a repeating character. We then shrink the window from the left (i) until the repeating character is removed from the window.

Time and Space Complexity:

The time complexity of this solution is O(n), where n is the length of the string, because each character is visited at most twice (once by i and once by j). The space complexity is O(min(m, n)), where m is the size of the character set and n is the length of the string. In the worst case (all unique characters), the space used is proportional to the length of the string.

Beyond the Basic Solution

The sliding window approach is efficient and widely applicable. However, consider these enhancements:

• Handling edge cases: Explicitly handle empty strings or strings containing only one character.
• Different Data Structures: While dictionaries are ideal for this, other data structures (like sets if you don't need index tracking) could be used, potentially affecting performance slightly.

This enhanced understanding, combined with the insights from the common Stack Overflow approaches, provides a comprehensive guide to solving the longest substring without repeating characters problem efficiently and effectively. Remember to always cite your sources when drawing inspiration from online communities like Stack Overflow.