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Sliding Window

Visualize the Sliding Window technique to efficiently process subarrays.

Current Sum

0

Max Sum

-

Ready to find max sum.
2
0
1
1
5
2
1
3
3
4
2
5
9
6
7
7
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Intuition

Instead of re-calculating the sum of every subarray from scratch (O(n*k)), we can "slide" the window by one position: add the new element entering the window and subtract the element leaving it. This reduces the complexity to O(n).

Concept

  • Window: A range of elements [start, end].
  • Slide: Move start and end by 1.
  • Optimization: Reuse the result of the previous window.

How it Works

Algorithm:

  1. Calculate sum of first k elements.
  2. Set maxSum = currentSum.
  3. Loop from i = k to n-1:
  4. currentSum += arr[i] (Add new)
  5. currentSum -= arr[i-k] (Remove old)
  6. Update maxSum if needed.

Step-by-Step Breakdown

Watch the visualization:

  • Observe the window moving one step at a time.
  • See how the sum updates instantly by just +1 and -1 operation.

When to Use

  • Finding subarrays of fixed size K with specific properties (max sum, min avg).
  • Finding longest/shortest subarray with specific properties (dynamic window).
  • String problems (longest substring without repeating characters).

When NOT to Use

  • When the problem requires non-contiguous elements (subsequences).

How to Identify

"Subarray of size K", "Longest substring", "Consecutive elements".

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