Compară metode
Examinează metodele selectate una lângă alta; rândurile care diferă sunt evidențiate.
| Programare Dinamică Deterministică× | Programarea Dinamică Stocastică× | |
|---|---|---|
| Domeniu | Simulare | Simulare |
| Familie | Process / pipeline | Process / pipeline |
| Anul apariției | 1957 | 1957 |
| Autorul original≠ | Richard E. Bellman | Bellman, R.; formalized for stochastic settings by Puterman, M. L. |
| Tip≠ | Exact sequential optimization algorithm | Sequential optimization under uncertainty |
| Sursa seminală≠ | Bellman, R. E. (1957). Dynamic Programming. Princeton University Press, Princeton, NJ. ISBN: 9780691079516 | Bellman, R. (1957). Dynamic Programming. Princeton University Press, Princeton, NJ. ISBN: 9780486428093 |
| Denumiri alternative | DDP, Deterministic DP, Classical Dynamic Programming, Bellman Dynamic Programming | SDP, Markov Decision Process, MDP, Stochastic DP |
| Înrudite | 6 | 6 |
| Rezumat≠ | Deterministic Dynamic Programming (DDP) is a mathematical optimization technique that decomposes a multi-stage decision problem into a sequence of simpler subproblems, solving them exactly when all system parameters — transition functions, costs, and rewards — are known with certainty. It guarantees a globally optimal policy via Bellman's principle of optimality. | 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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