You are given a job that has been divided into N tasks. The task cannot be divided any further. Each of the N tasks takes Si number of seconds to complete. Your job will be completed when all your tasks are completed. You have K workers at your disposal to help you complete the tasks. But as per the nature of the job, a worker can only be allocated continuous tasks. A worker can work only on a single task at any given point in time. However, the workers can work in parallel on different tasks. You have to find the minimum possible time in which you can complete the job.Input FormatThe first line of input contains T - the number of test cases. It's followed by 2T lines. The first line of each test case contains N and K - the number of tasks and available workers for the current job, separated by space. The next line contains N positive integers - denoting the time taken to complete the ith task.Output FormatFor each test case, print the minimum possible time in which you can complete the job, separated by a new line.Constraints50 points1 <= N,K <= 20100 points1 <= N,K <= 10000General Constraints1 <= T <= 501 <= Si <= 103ExampleInput610 31 10 13 4 5 12 23 12 18 88 417 27 22 45 26 32 45 162 274 617 374 24 61 81 66 76 512 154 104 215 30 10 50Output3866741596455ExplanationTest Case 1: The arrangement for which we can achieve minimum possible time is as follows:[1 10 13 4 5] - Worker 1[12 23] - Worker 2[12 18 8] - Worker 3Test Case 2: The arrangement for which we can achieve minimum possible time is as follows:[17 27 22] - Worker 1[45] - Worker 2[26 32] - Worker 3

Given a set of N jobs where each jobi has a deadline and profit associated with it.Each job takes 1 unit of time to complete and only one job can be scheduled at a time. We earn the profit associated with job if and only if the job is completed by its deadline.Find the number of jobs done and the maximum profit.Note: Jobs will be given in the form (Jobid, Deadline, Profit) associated with that Job.

The following table shows jobs to be processed at a machine. Determine the processing sequence that will minimize total number of late jobs.   Job Processing Time (days) Due Date (days)   A 3   4    B 2   5    C 5 10   D 2 11   E 4 13   F 9 201. Job is the first job that is late in the given sequence.2. Job has the largest processing time out of these 5 jobs.3. Optimal sequence is with on

he minimum time required to complete the taskThe maximum time required to complete the taskThe most likely time required to complete the taskThe earliest possible start time for the task

You are given an integer N denoting an N×N matrix. Initially, each cell of the matrix isempty. You are given K tasks. In each task, you are given a cell (i, j) where cell (i, j)represents the ithrow and jth column of the given matrix.You have to perform each task sequentially in the given order. Each task is describedin cell (i, j). For each task, you have to place X in each cell of row i and each cellcolumn j. After you complete each task, you are required to print the number ofempty cells in the matrix.

You have n processors each having 4 cores and n * 4 tasks that need to be executed such that each core should perform only one task.Given a 0-indexed integer array processorTime representing the time at which each processor becomes available for the first time and a 0-indexed integer array tasks representing the time it takes to execute each task, return the minimum time when all of the tasks have been executed by the processors.Note: Each core executes the task independently of the others. Example 1:Input: processorTime = [8,10], tasks = [2,2,3,1,8,7,4,5]Output: 16Explanation: It's optimal to assign the tasks at indexes 4, 5, 6, 7 to the first processor which becomes available at time = 8, and the tasks at indexes 0, 1, 2, 3 to the second processor which becomes available at time = 10. Time taken by the first processor to finish execution of all tasks = max(8 + 8, 8 + 7, 8 + 4, 8 + 5) = 16.Time taken by the second processor to finish execution of all tasks = max(10 + 2, 10 + 2, 10 + 3, 10 + 1) = 13.Hence, it can be shown that the minimum time taken to execute all the tasks is 16.Example 2:Input: processorTime = [10,20], tasks = [2,3,1,2,5,8,4,3]Output: 23Explanation: It's optimal to assign the tasks at indexes 1, 4, 5, 6 to the first processor which becomes available at time = 10, and the tasks at indexes 0, 2, 3, 7 to the second processor which becomes available at time = 20.Time taken by the first processor to finish execution of all tasks = max(10 + 3, 10 + 5, 10 + 8, 10 + 4) = 18.Time taken by the second processor to finish execution of all tasks = max(20 + 2, 20 + 1, 20 + 2, 20 + 3) = 23.Hence, it can be shown that the minimum time taken to execute all the tasks is 23. Constraints:1 <= n == processorTime.length <= 250001 <= tasks.length <= 1050 <= processorTime[i] <= 1091 <= tasks[i] <= 109tasks.length == 4 * n