What is the best approach to solve Sliding Window Maximum?

The optimal approach for Sliding Window Maximum is the "Optimal — Monotonic Deque" technique. It runs in O(n) time and uses O(k) space. Maintain a deque of indices in decreasing order of their values. For each element: (1) remove indices outside the window from the front. (2) Remove from the back any indices with values smaller than c

What is the time complexity of Sliding Window Maximum?

The optimal solution for Sliding Window Maximum has a time complexity of O(n) and a space complexity of O(k), achieved using the Monotonic Deque pattern.

Is Sliding Window Maximum a Hard LeetCode problem?

Yes, LeetCode #239 (Sliding Window Maximum) is rated Hard. It uses the Monotonic Deque pattern and is commonly asked in interviews at Amazon, Google, Microsoft.

How to solve Sliding Window Maximum in Python, Java, C++, and JavaScript?

LumiChats provides complete solutions for Sliding Window Maximum in Python, Java, C++, and JavaScript, along with step-by-step visual animations and time/space complexity analysis. All solutions are free to view.

#239HardMonotonic Deque

Sliding Window Maximum

Maintain a decreasing deque of indices — O(1) window max at each step.

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Problem Statement

You are given an integer array nums and a sliding window of size k which moves from the left of the array to the right. Return the max sliding window — the maximum of each window.

Examples

Example 1

Input:nums = [1,3,-1,-3,5,3,6,7], k = 3

Output:[3,3,5,5,6,7]

Example 2

Input:nums = [1], k = 1

Output:[1]

Constraints

1 ≤ nums.length ≤ 10⁵
-10⁴ ≤ nums[i] ≤ 10⁴
1 ≤ k ≤ nums.length

Solutions

Brute Force — Scan each window

TimeO(n×k)SpaceO(1)

For each window position, scan k elements to find the max. O(n×k) — too slow for large inputs.

Visual Animation

def maxSlidingWindow(nums: list[int], k: int) -> list[int]:
    return [max(nums[i:i+k]) for i in range(len(nums)-k+1)]

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