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Master the Sliding Window Technique: Pattern Recognition Guide

Learn how to identify and solve sliding window problems efficiently. Includes common patterns and practice problems.

December 10, 2024
13 min read
ArraysTwo PointersPattern Recognition

Master the Sliding Window Technique: Pattern Recognition Guide


The sliding window technique is one of the most powerful patterns for solving array and string problems efficiently.


What is Sliding Window?


A sliding window maintains a subset of elements that "slides" through the array, avoiding redundant calculations.


Fixed Size Window


When the window size is fixed.


def max_sum_subarray(arr, k):
    window_sum = sum(arr[:k])
    max_sum = window_sum
    
    for i in range(k, len(arr)):
        window_sum = window_sum - arr[i - k] + arr[i]
        max_sum = max(max_sum, window_sum)
    
    return max_sum

Time complexity is O(n).

Space complexity is O(1).


Variable Size Window


When the window size varies based on conditions.


def longest_substring_without_repeating(s):
    char_map = {}
    left = 0
    max_len = 0
    
    for right in range(len(s)):
        if s[right] in char_map:
            left = max(left, char_map[s[right]] + 1)
        
        char_map[s[right]] = right
        max_len = max(max_len, right - left + 1)
    
    return max_len

Common Patterns


  • Fixed windows track a max or min inside a subarray of fixed size k.
  • Variable windows grow or shrink until a condition holds on a contiguous range.
  • Two pointers often pair with either style when the array is sorted or monotone.

  • When to Use


  • Subarray/substring problems
  • Need to track a window of elements
  • Optimization problems on contiguous sequences

  • Master this pattern to solve many array problems efficiently!


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