Magnetic Force Between Two Balls | Simple Thought Process | Leetcode 1552 | codestorywithMIK
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This is the 30th Video of our Playlist "Binary Search : Popular Interview Problems" by codestorywithMIK
In this video we will try to solve a very good and famous Problem on Binary Search on Answer topic : Magnetic Force Between Two Balls | Simple Thought Process | Leetcode 1552 | 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 : Magnetic Force Between Two Balls | Simple Thought Process | Leetcode 1552 | codestorywithMIK
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My solutions on Github(C++ & JAVA) : https://github.com/MAZHARMIK/Intervie...
Leetcode Link : https://leetcode.com/problems/magneti...
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Summary :
The approach to finding the maximum minimum distance for placing m balls in an array of positions involves using a combination of binary search and a greedy placement strategy:
Sorting the Positions:
The positions array is sorted to facilitate the calculation of distances between positions.
Binary Search on Distance:
The binary search is used to efficiently find the maximum minimum distance. The search range (minForce to maxForce) starts from 1 to the difference between the maximum and minimum values in the positions array.
Greedy Placement Check:
For a given candidate distance (midpoint in binary search), a helper method possibleToPlace checks if it is possible to place all m balls such that the distance between any two consecutive balls is at least this candidate distance.
The first ball is placed at the first position. Subsequent balls are placed greedily at the next position that is at least the candidate distance away from the last placed ball.
Adjusting the Search Range:
If it is possible to place all m balls with the candidate distance, the search continues with a larger distance by updating the lower bound (minForce).
If it is not possible, the search continues with a smaller distance by updating the upper bound (maxForce).
Result:
The maximum value of the candidate distance that allows placing all m balls is recorded as the result.
This approach ensures that the solution is found efficiently, leveraging the power of binary search to minimize the number of distance checks required.
✨ Timelines✨
00:00 - Introduction
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