readme.md

Intuition

To solve this problem, we can use a sliding window approach to efficiently find the maximum length of a substring of s that can be changed to match t with a total cost less than or equal to maxCost. The sliding window technique is suitable here because it allows us to maintain a window of valid substrings and expand or contract the window as needed based on the cost constraints.

Approach

1. Sliding Window Technique: Use two pointers to represent the start and end of the current window (substring) of s and t.
2. Calculate Cost: Maintain the total cost of converting the current window of s to t.
3. Expand and Contract Window: Expand the window by moving the end pointer. If the total cost exceeds maxCost, contract the window by moving the start pointer until the total cost is within maxCost.
4. Track Maximum Length: Track the maximum length of valid substrings encountered during the process.

Complexity

• Time complexity: $O(n)$, where $n$ is the length of the strings s and t. Each character is processed at most twice (once when expanding the window and once when contracting it).

• Space complexity: $O(1)$, as only a fixed amount of extra space is used for pointers and counters.

Code