The sliding window technique helps solve problems involving subarrays or sublists by maintaining a window over elements and moving it efficiently to find results.

Sliding window techniques are highly efficient for solving problems involving subarrays or sublists with fixed or variable lengths. The core idea is to maintain a 'window' over a section of the array, moving it step-by-step to cover all relevant segments without recalculating values for every element. Sliding windows can be fixed (e.g., finding the maximum sum of any contiguous subarray of length k) or dynamic, where the window size changes based on conditions. This technique is particularly useful in problems related to contiguous subarrays, such as finding maximum or minimum sums, or checking for specific conditions within a segment. Sliding window reduces time complexity by preventing redundant calculations, often turning O(n^2) operations into O(n), making it an essential optimization tool in competitive programming.