Category: C++

This is the biggest shift in the entire sliding window series. Every problem in Parts 1–4 had a fixed window size. You were always told: look at every subarray of size k. The window slid one step at a time — add one element, remove one element — like clockwork. The challenge was in what you tracked inside the window, not in the window itself. In this post, there is no k. The window size is not given. Instead, the window expands and contracts dynamically, and finding the right size is the core of the problem. How Variable-Size Windows Work READ MORE

In Parts 1–3, every sliding window problem followed a single template: you were given a window size k, you maintained a running sum or count, and you slid one step at a time. The only variation was the evaluation logic — track the max, count matches, or maintain a monotonic deque. The window itself was always dumb: it just held a number. Starting with this post, the pattern evolves. The state inside the window gets richer. Instead of a single integer (sum, count), the window now tracks a full frequency distribution or a sorted data structure. The window still slides READ MORE

In Part 1 and Part 2, every problem had a clean O(1) update per window slide — add an element, remove an element, done. That worked because we were tracking sums or counts, which are trivially updatable. But what if you need the maximum of each window? You can’t just subtract the outgoing element from “the max” — the max might not change, or it might have been the element you just removed. A naive approach would scan the entire window for the new max each time, giving O(nk). For large inputs, that’s too slow. This is where the monotonic READ MORE

In Part 1, we covered the core fixed-size sliding window pattern: maintain a running sum, slide one element at a time, evaluate each window in O(1). The three problems we solved were almost identical in structure. Now things get more interesting. The four problems in this post still use a fixed-size window, but each one adds a twist to what you track or how you evaluate the window. The underlying mechanics remain the same — the challenge is in decomposing the problem to recognize where the sliding window fits. Problem 4: Diet Plan Performance (LC 1176) Difficulty: Easy Problem: A READ MORE

If you’re preparing for coding interviews or just want to sharpen your algorithm skills, the sliding window technique is one of the most important patterns to master. It shows up everywhere — from easy warm-ups to hard interview favorites — and once you see the pattern, you’ll recognize it instantly. In this 3-part series, I’ll walk you through 8 LeetCode problems that all use sliding window, progressing from the simplest form to more advanced variations. By the end, you’ll have a clear mental model for when and how to apply this technique. What Is the Sliding Window Pattern? Imagine you READ MORE