The sliding window technique involves maintaining a subset of data as a window and moving it across the data structure to optimize time complexity, often used in problems like subarray sums.

The sliding window technique is a popular approach in competitive programming for solving problems involving subarrays, substrings, or contiguous sections of data. Instead of recalculating the sum or product of every possible subarray from scratch, the sliding window approach allows you to maintain a 'window' of data that shifts over the input array. For example, in a problem where you need to find the maximum sum of a subarray of size k, you can compute the sum of the first subarray, then slide the window one element at a time, subtracting the element that is left behind and adding the new element. This reduces the time complexity from O(n^2) to O(n), making it much more efficient for large inputs. The sliding window technique can be either fixed-size or variable-size, depending on the problem. Fixed-size windows are common in problems like finding the maximum sum of a subarray of length k, while variable-size windows are used in problems like finding the smallest subarray with a sum greater than or equal to a target value. Mastering this technique will help you solve a wide range of problems more efficiently by reducing redundant calculations and optimizing time complexity.