Minimum Cost for Cutting Cake I & II | Thought Process | Leetcode 3218 | 3219 | codestorywithMIK
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This is the 36th Video of our Playlist "GREEDY TECHNIQUE : Popular Interview Problems" by codestorywithMIK
In this video we will try to solve a very good Greedy Problem : Minimum Cost for Cutting Cake I & II | Simple Thought Process | Leetcode 3218 | 3219 | codestorywithMIK
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Problem Name : Minimum Cost for Cutting Cake I & II | Simple Thought Process | Leetcode 3218 | 3219 | codestorywithMIK
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Summary :
The given approach is designed to compute the minimum cost of cutting a grid of size m x n using specified horizontal and vertical cuts. Here's a summary of the approach:
Input Preparation: The function minimumCost takes parameters m and n representing the dimensions of the grid, and two vectors horizontalCut and verticalCut which contain the positions where the grid will be cut horizontally and vertically, respectively.
Sorting: Both horizontalCut and verticalCut vectors are sorted in descending order. This ensures that we start cutting from the largest to the smallest segment, which helps in minimizing costs effectively.
Initialization:
i and j are initialized to traverse through horizontalCut and verticalCut respectively.
horizontalPieces and verticalPieces are initialized to 1, representing the current number of horizontal and vertical segments.
Cost Calculation:
Using a while loop, the algorithm compares the current largest segment available in horizontalCut[i] and verticalCut[j].
If horizontalCut[i] is larger or equal to verticalCut[j], it adds the cost of cutting horizontalCut[i] multiplied by verticalPieces to the result. Then it increments horizontalPieces and moves to the next segment in horizontalCut.
Otherwise, it adds the cost of cutting verticalCut[j] multiplied by horizontalPieces to the result, increments verticalPieces, and moves to the next segment in verticalCut.
Completion of Cutting:
After the while loop, any remaining segments in horizontalCut or verticalCut are processed using two separate while loops to ensure all segments are accounted for in the final cost calculation.
Result: The function returns result, which represents the minimum cost required to completely cut the grid according to the specified cuts.
This approach effectively computes the minimum cost by dynamically adjusting the number of pieces and accumulating costs based on the largest available segment at each step, ensuring optimal cutting strategy.
✨ Timelines✨
00:00 - Introduction
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