Comparar métodos
Examine os métodos selecionados lado a lado; as linhas que diferem ficam destacadas.
| Algoritmo Push-Relabel× | Algoritmo de Ford-Fulkerson× | |
|---|---|---|
| Área | Pesquisa operacional | Pesquisa operacional |
| Família | Machine learning | Machine learning |
| Ano de origem≠ | 1988 | 1956 |
| Autor original≠ | Andrew V. Goldberg and Robert E. Tarjan | Lester R. Ford and Delbert R. Fulkerson |
| Tipo | algorithm | algorithm |
| Fonte seminal≠ | Goldberg, A. V., & Tarjan, R. E. (1988). A new approach to the maximum flow problem. Journal of the ACM, 35(4), 921-940. DOI ↗ | Ford, L. R., & Fulkerson, D. R. (1956). Maximal flow through a network. Canadian Journal of Mathematics, 8(3), 399-404. DOI ↗ |
| Outros nomes | preflow-push algorithm, Goldberg-Tarjan algorithm | Ford-Fulkerson method, augmenting path method |
| Relacionados≠ | 3 | 4 |
| Resumo≠ | The Push-Relabel Algorithm, developed by Andrew V. Goldberg and Robert E. Tarjan in 1988, is a highly efficient method for computing maximum flow in networks. Unlike augmenting path methods, it maintains a preflow and uses local push and global relabeling operations to drive flow toward the sink, achieving superior worst-case complexity. | The Ford-Fulkerson Algorithm, developed by Lester R. Ford and Delbert R. Fulkerson in 1956, is a foundational method for computing the maximum flow in a flow network. It finds the maximum amount of flow that can be sent from a source to a sink through a directed graph with capacity constraints on edges. |
| ScholarGateConjunto de dados ↗ |
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