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  2. Greedy algorithm - Wikipedia

    en.wikipedia.org/wiki/Greedy_algorithm

    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.

  3. Change-making problem - Wikipedia

    en.wikipedia.org/wiki/Change-making_problem

    One variation of this problem assumes that the people making change will use the "greedy algorithm" for making change, even when that requires more than the minimum number of coins. Most current currencies use a 1-2-5 series , but some other set of denominations would require fewer denominations of coins or a smaller average number of coins to ...

  4. Greedy randomized adaptive search procedure - Wikipedia

    en.wikipedia.org/wiki/Greedy_randomized_adaptive...

    The greedy randomized adaptive search procedure (also known as GRASP) is a metaheuristic algorithm commonly applied to combinatorial optimization problems. GRASP typically consists of iterations made up from successive constructions of a greedy randomized solution and subsequent iterative improvements of it through a local search . [ 1 ]

  5. Charging argument - Wikipedia

    en.wikipedia.org/wiki/Charging_Argument

    To show that the earliest finish time algorithm is a 2-approximation algorithm using the charging argument, h must be shown to be a two-to-one function mapping intervals in OPT(I) to those in EFT(I). Suppose J is an arbitrary interval in OPT(I). Show that h is a function mapping OPT(I) to EFT(I).

  6. Category:Greedy algorithms - Wikipedia

    en.wikipedia.org/wiki/Category:Greedy_algorithms

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  7. Greedy number partitioning - Wikipedia

    en.wikipedia.org/wiki/Greedy_number_partitioning

    In computer science, greedy number partitioning is a class of greedy algorithms for multiway number partitioning. The input to the algorithm is a set S of numbers, and a parameter k. The required output is a partition of S into k subsets, such that the sums in the subsets are as nearly equal as possible. Greedy algorithms process the numbers ...

  8. Optimal substructure - Wikipedia

    en.wikipedia.org/wiki/Optimal_substructure

    Typically, a greedy algorithm is used to solve a problem with optimal substructure if it can be proven by induction that this is optimal at each step. [1] Otherwise, provided the problem exhibits overlapping subproblems as well, divide-and-conquer methods or dynamic programming may be used.

  9. Greedoid - Wikipedia

    en.wikipedia.org/wiki/Greedoid

    A greedy algorithm is optimal for every R-compatible linear objective function over a greedoid. The intuition behind this proposition is that, during the iterative process, each optimal exchange of minimum weight is made possible by the exchange property, and optimal results are obtainable from the feasible sets in the underlying greedoid.