Job Execution
MathWorks● MediumNEW GRADOAProblem statement
There are n jobs that can be executed in parallel on a processor, where the execution time
of the jth job is executionTime[j]. To speed up execution, the following strategy
is used.
In one operation, a job is chosen, the major job, and is executed for x seconds. All other jobs
are executed for y seconds where y < x.
A job is complete when it has been executed for at least executionTime[i] seconds, then it exits the pool. Find the minimum number of operations in which the processor can completely execute all the jobs if run optimally.
Function
getMinimumOperations(executionTime: int[], x: int, y: int) → int
Complete the function getMinimumOperations in the editor below.
getMinimumOperations has the following parameters:
int executionTime[n]: the execution times of each jobint x: the time for which the major job is executedint y: the time for which all other jobs are executed
Returns
int: the minimum number of operations in which the processor can complete the jobs
Examples
Example 1
executionTime = [3, 4, 1, 7, 6]x = 4y = 2return = 3The following strategy is optimal using 1-based indexing. 1. Choose job 4 as the major job and reduce the execution times of job 4 by x and of other jobs by y. So, executionTime = [1, 2, -1, 1, 4], Job 3 is complete, so it is removed. 2. Choose job 5 as the major job and reduce the execution times of job 5 by x and of other jobs by y. So, executionTime = [-1, 0, -3, -1, 2], Jobs 1, 2, and 4 are now complete. 3. Choose job 5, executionTime = [-3, -2, -5, -3, -4], Job 5 is complete. It takes 3 operations to execute all the jobs so the answer is 3.
Constraints
- 1 ≤ n ≤ 10^5
- 1 ≤ executionTime[i] ≤ 10^9^
- 1 ≤ y < x ≤ 10^9