Find the Power of K-Size Subarrays I | Why Monotonic Deque | Dry Run|Leetcode 3254 |codestorywithMIK
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This is the 29th Video of our Playlist "Sliding Window : Popular Interview Problems" by codestorywithMIK
In this video we will try to solve a classic Sliding Window Problem : Find the Power of K-Size Subarrays I | Using Monotonic Deque | Dry Run | Leetcode 3254 | codestorywithMIK
I will explain the intuition so easily that you will never forget and start seeing this as cakewalk EASYYY.
We will do live coding after explanation and see if we are able to pass all the test cases.
Also, please note that my Github solution link below contains both C++ as well as JAVA code.
Problem Name : Find the Power of K-Size Subarrays I | Using Monotonic Deque | Dry Run | Leetcode 3254 | codestorywithMIK
Company Tags : will update later
My solutions on Github(C++ & JAVA) - https://github.com/MAZHARMIK/Interview_DS_Algo/blob/master/Sliding Window/Find the Power of K-Size Subarrays I.cpp
Leetcode Link : https://leetcode.com/problems/find-the-power-of-k-size-subarrays-i
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Summary :
The approach uses a monotonic deque to efficiently process the array in a sliding window manner. Here's a summary:
Sliding Window: The window size is k, and the goal is to maintain relevant elements within the deque for each window.
Deque Properties:
The deque stores consecutive numbers within the window in a monotonic order.
If a new element is not consecutive to the last deque element, the deque is cleared.
Result Computation:
If the deque contains exactly k elements at the end of a window, the last element (due to the monotonic property) is added to the result.
Otherwise, -1 is added if the deque size is less than k.
Efficiency: By using a deque to store and update window elements, the solution ensures that each element is added and removed at most once, leading to an O(n) time complexity.
✨ Timelines✨
00:00 - Introduction
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