Sliding Window Algorithm Explained: Cut Array Problem Complexity from O(N²) to O(N)

The sliding window technique is a key algorithmic pattern used to optimize problems involving contiguous subarrays or substrings in arrays and strings. Instead of using brute-force nested loops with quadratic time complexity, the method reuses prior computations so each element is processed at most twice, achieving linear O(N) time. The pattern comes in two main forms: a fixed-length window of constant size K, and a variable-length window that expands or contracts based on conditions. It is best applied when problems involve contiguous data and monotonic metrics like sums or character frequencies, but is unsuitable for arrays with negative numbers or non-contiguous subsequences. Real-world use cases include calculating moving stock price averages and managing API rate limits in network systems.