Cracking Problems With Sliding Windows

Memorizing coding problems is brittle. Understanding patterns makes you adaptable. The sliding window is one such pattern.

What It Is

A sliding window processes a sequence incrementally instead of recomputing each time:

• Fixed window — constant size
• Dynamic window — grows or shrinks based on conditions

It turns many O(n²) solutions into O(n).

Why Patterns > Memorization

Memorized solutions break if the problem changes. Understanding patterns lets you:

• Adapt to variations
• Write clean, maintainable code
• Spot similar problems quickly

Example: Max Sum Subarray of Size k

Brute Force:

def max_sum_brute(arr, k):
    return max(sum(arr[i:i+k]) for i in range(len(arr)-k+1))

O(n*k)

Sliding Window:

def max_sum_window(arr, k):
    window = sum(arr[:k])
    max_sum = window
    for i in range(k, len(arr)):
        window += arr[i] - arr[i-k]
        max_sum = max(max_sum, window)
    return max_sum

O(n) — reuse info instead of recomputing.

Spotting Sliding Window Problems

• Contiguous subarrays/strings
• Max/min/sum/count queries
• Repeated computations over ranges

Tips

• Understand the concept first.
• Visualize the window.
• Practice fixed vs dynamic windows.
• Explain it aloud to reinforce understanding.

Conclusion

Sliding window = flexible tool, not a trick. Focus on patterns, not memorization. Memorize less. Understand more.