Maximize the Minimum Game Score | Super Detailed | Dry Runs | Leetcode 3449 | codestorywithMIK

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Published on February 13, 2025 2:58:22 PM ● Video Link: https://www.youtube.com/watch?v=mKDi2RYriLE

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Hi Everyone, this is the 36th video of our Playlist "Binary Search : Popular Interview Problems".
Now we will be solving a good practice problem on Binary Search - Maximize the Minimum Game Score | Super Detailed | Dry Runs | Leetcode 3449 | codestorywithMIK
This problem falls under the category - 'Binary Search on Answer" which is one of the very famous topics.
This was the 4th Problem of Sunday Weekly Leetcode Contest held on 9th Feb, 2025.

Problem Name : Maximize the Minimum Game Score | Super Detailed | Dry Runs | Leetcode 3449 | codestorywithMIK
Company Tags : will update later
My solutions on Github(C++ & JAVA) - https://github.com/MAZHARMIK/Interview_DS_Algo/blob/master/Arrays/Binary Search/Maximize the Minimum Game Score.cpp
Leetcode Link : https://leetcode.com/problems/maximize-the-minimum-game-score/description/


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Video Summary :
This problem follows the "Binary Search on Answer" technique, which is useful when solving optimization problems that ask for maximizing the minimum or minimizing the maximum value. Here, we need to find the maximum possible minimum score that can be achieved after at most m moves. Instead of checking each possible score linearly, we apply binary search on the possible score range.
For a given target score, we check if it's achievable within m moves using a helper function that simulates the gameplay. The function determines the number of moves needed by strategically advancing through game points, either by efficiently reusing previous progress or making standard moves. If a target score is possible, we try for a higher one; otherwise, we adjust the search range downward. This ensures we efficiently converge to the optimal solution.


✨ Timelines✨
00:00 - Introduction
00:21 - Motivation
01:21 - Problem Explanation
04:14 - Detailed Thought Process Example :1
07:04 - Dry Run Example : 2
00:00 - Coding it up
00:00 - Time & Space Complexity

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