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What is the sliding window technique?

Q: What is the sliding window technique?

**Intuition.** Many problems ask about a *contiguous* run of elements. Rather than recompute each window from scratch, you **slide** a window across the data — adding the new element on the right and removing the old one on the left — reusing the previous result. O(n²) → **O(n)**. max sum of a window of size 3 <rect x="80" y="46" width="56" height="44" rx="6" fill="currentColor" fill-opacity="0.05" stroke="currentColor" stroke-opacity="0.25"/

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Max sum of a fixed window

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def max_window_sum(arr, k):
    window = sum(arr[:k])
    best = window
    for i in range(k, len(arr)):
        window += arr[i] - arr[i - k]   # add new, drop old
        best = max(best, window)
    return best


# --- demo ---
print(max_window_sum([1, 4, 2, 9, 3, 5, 1], 3))   # 17  -> [9,3,5]

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