Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Стохастическое динамическое программирование× | Стохастическое линейное программирование× | |
|---|---|---|
| Область | Имитационное моделирование | Имитационное моделирование |
| Семейство | Process / pipeline | Process / pipeline |
| Год появления≠ | 1957 | 1955 |
| Автор метода≠ | Bellman, R.; formalized for stochastic settings by Puterman, M. L. | George B. Dantzig |
| Тип≠ | Sequential optimization under uncertainty | Stochastic optimization model |
| Основополагающий источник≠ | Bellman, R. (1957). Dynamic Programming. Princeton University Press, Princeton, NJ. ISBN: 9780486428093 | Dantzig, G. B., & Madansky, A. (1961). On the solution of two-stage linear programs under uncertainty. Proceedings of the Fourth Berkeley Symposium on Mathematical Statistics and Probability, 1, 165–176. link ↗ |
| Другие названия | SDP, Markov Decision Process, MDP, Stochastic DP | SLP, Stochastic LP, Linear Programming under Uncertainty, Two-Stage SLP |
| Связанные≠ | 6 | 5 |
| Сводка≠ | Stochastic Dynamic Programming (SDP) is a mathematical optimization framework for sequential decision problems where outcomes are partly random. It extends Bellman's principle of optimality to stochastic environments, representing problems as Markov Decision Processes (MDPs) and computing optimal policies by solving recursive value equations over states and time periods. | Stochastic Linear Programming (SLP) extends classical linear programming to settings where some model parameters — costs, demands, resource availability — are uncertain and modeled as random variables. By optimizing expected costs over a probability distribution of scenarios, SLP produces decisions that remain feasible and near-optimal across a range of possible futures rather than for a single assumed state of the world. |
| ScholarGateНабор данных ↗ |
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