Comparer des méthodes
Examinez les méthodes sélectionnées côte à côte ; les lignes qui diffèrent sont mises en évidence.
| Programmation dynamique par scénarios de politique× | Programmation dynamique stochastique× | |
|---|---|---|
| Domaine | Simulation | Simulation |
| Famille | Process / pipeline | Process / pipeline |
| Année d'origine | 1957 | 1957 |
| Auteur d'origine≠ | Bellman, Richard E. | Bellman, R.; formalized for stochastic settings by Puterman, M. L. |
| Type≠ | Sequential optimization with scenario branching | Sequential optimization under uncertainty |
| Source fondatrice | Bellman, R. (1957). Dynamic Programming. Princeton University Press, Princeton, NJ. ISBN: 9780691079516 | Bellman, R. (1957). Dynamic Programming. Princeton University Press, Princeton, NJ. ISBN: 9780486428093 |
| Alias | PSDP, Policy-Scenario DP, Scenario-Based Dynamic Programming, Policy DP | SDP, Markov Decision Process, MDP, Stochastic DP |
| Apparentées≠ | 5 | 6 |
| Résumé≠ | Policy Scenario Dynamic Programming (PSDP) applies Bellman's recursive optimization framework to a set of pre-specified policy scenarios, enabling decision-makers to compare staged, sequential decisions under distinct future conditions. It decomposes a complex, multi-period policy choice into tractable sub-problems solved backward through time, yielding optimal action sequences for each scenario and a structured basis for scenario comparison. | 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. |
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