Compară metode
Examinează metodele selectate una lângă alta; rândurile care diferă sunt evidențiate.
| Programare Dinamică Multi-Obiectiv× | Programarea Dinamică Stocastică× | |
|---|---|---|
| Domeniu | Simulare | Simulare |
| Familie | Process / pipeline | Process / pipeline |
| Anul apariției≠ | 1957-1975 | 1957 |
| Autorul original≠ | Extension of Bellman (1957); formalized by multiple authors from 1970s onward | Bellman, R.; formalized for stochastic settings by Puterman, M. L. |
| Tip≠ | Exact optimization — recursive multi-objective decomposition | Sequential optimization under uncertainty |
| Sursa seminală | Bellman, R. (1957). Dynamic Programming. Princeton University Press, Princeton, NJ. ISBN: 9780691079516 | Bellman, R. (1957). Dynamic Programming. Princeton University Press, Princeton, NJ. ISBN: 9780486428093 |
| Denumiri alternative | MODP, Multi-criteria dynamic programming, Vector dynamic programming, Pareto dynamic programming | SDP, Markov Decision Process, MDP, Stochastic DP |
| Înrudite≠ | 5 | 6 |
| Rezumat≠ | Multi-Objective Dynamic Programming (MODP) extends Bellman's classical dynamic programming to settings where a decision-maker must optimize several competing objectives simultaneously across a sequence of stages. Rather than a single optimal policy, it produces a Pareto-optimal set of policies — each representing a distinct trade-off profile — by propagating vector-valued value functions backward through the state space. | 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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