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:** O(n)

**Space Complexity:** 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

1. **Fixed Window:** Maximum/minimum in subarray of size k

2. **Variable Window:** Longest/shortest subarray satisfying condition

3. **Two Pointers:** Often combined with sliding window

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!