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The following greedy algorithm, called Earliest deadline first scheduling, does find the optimal solution for unweighted single-interval scheduling: Select the interval, x, with the earliest finishing time. Remove x, and all intervals intersecting x, from the set of candidate intervals. Repeat until the set of candidate intervals is empty.
Longest-processing-time-first (LPT) is a greedy algorithm for job scheduling. The input to the algorithm is a set of jobs, each of which has a specific processing-time. There is also a number m specifying the number of machines that can process the jobs. The LPT algorithm works as follows:
The activity selection problem is also known as the Interval scheduling maximization problem (ISMP), which is a special type of the more general Interval Scheduling problem. A classic application of this problem is in scheduling a room for multiple competing events, each having its own time requirements (start and end time), and many more arise ...
interval order: Each job has an interval [s x,e x) and job is a predecessor of if and only if the end of the interval of is strictly less than the start of the interval for .= In the presence of a precedence relation one might in addition assume time lags. The time lag between two jobs is the amount of time that must be waited after the first ...
This algorithm runs in time O(n). The specific list-scheduling algorithm called Longest Processing Time First (LPT), which sorts the jobs by descending length, is a / / approximation for identical machines. [4]: sec.5 It is also called greedy number partitioning.
Otherwise, disregard the interval. The interval scheduling problem can be viewed as a profit maximization problem, where the number of intervals in the mutually compatible subset is the profit. The charging argument can be used to show that the earliest finish time algorithm is optimal for the interval scheduling problem.
Greedy algorithms determine the minimum number of coins to give while making change. These are the steps most people would take to emulate a greedy algorithm to represent 36 cents using only coins with values {1, 5, 10, 20}. The coin of the highest value, less than the remaining change owed, is the local optimum.
See interval scheduling for more information. An optimal graph coloring of the interval graph represents an assignment of resources that covers all of the requests with as few resources as possible; it can be found in polynomial time by a greedy coloring algorithm that colors the intervals in sorted order by their left endpoints. [17]