Information is present in the screenshot and below. Based on that need help in solving the code for this problem in python. The time complexity has to be as less as possible (nlogn or n at best, no n^2). Apply greedy algorithm. Include and use necessary libraries and packages. Make sure ALL test cases return expected outputs by providing output screenshots.
Information is present in the screenshot and below. Based on that need help in solving the code for this problem in python. The time complexity has to be as less as possible (nlogn or n at best, no n^2). Apply greedy algorithm. Include and use necessary libraries and packages. Make sure ALL test cases return expected outputs by providing output screenshots.
Information is present in the screenshot and below. Based on that need help in solving the code for this problem in python. The time complexity has to be as less as possible (nlogn or n at best, no n^2). Apply greedy algorithm. Include and use necessary libraries and packages. Make sure ALL test cases return expected outputs by providing output screenshots.
Information is present in the screenshot and below. Based on that need help in solving the code for this problem in python. The time complexity has to be as less as possible (nlogn or n at best, no n^2). Apply greedy algorithm. Include and use necessary libraries and packages. Make sure ALL test cases return expected outputs by providing output screenshots.
Output Format The output contains one line with a single integer: the minimum instability you can achieve modulo 10^9 + 7.
Sample Input 0 2 1 1 2 3 4 3
Sample Output 0 30
Sample Input 1 2 2 2 1 4 2 1 5 1
Sample Output 1 40
Sample Input 2 2 5 6 4 6 7 0 1 3 2 0 6 1 6 2 2
Sample Output 2 214
The actual code
""" This function solves a test case.
Parameters: l : int - # of length cutting points of quatrum cluster w : int - # of width cutting points of quatrum cluster d : int - # of depth cutting points of quatrum cluster length_costs : array-like - list of length cutting point instability factors width_costs : array-like - list of width cutting point instability factors depth_costs : array-like - list of depth cutting point instability factors
Returns: An integer indicating the smallest attainable instability after cutting the cluster down to 1 x 1 x 1 cubes """ def solve(l,w,d,length_costs,width_costs,depth_costs): MOD = int(1e9 + 7) # TODO
l,w,d = list(map(int,input().split(" "))) length_costs = [int(input()) for i in range(l)] width_costs = [int(input()) for i in range(w)] depth_costs = [int(input()) for i in range(d)]
Process or set of rules that allow for the solving of specific, well-defined computational problems through a specific series of commands. This topic is fundamental in computer science, especially with regard to artificial intelligence, databases, graphics, networking, operating systems, and security.
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