Porovnat metody
Prohlédněte si vybrané metody vedle sebe; řádky, které se liší, jsou zvýrazněny.
| Deterministické dynamické programování× | Programování se smíšenými celočíselnými proměnnými× | |
|---|---|---|
| Obor | Simulace | Simulace |
| Rodina | Process / pipeline | Process / pipeline |
| Rok vzniku≠ | 1957 | 1958–1960 |
| Tvůrce≠ | Richard E. Bellman | Ralph Gomory (branch-and-bound cuts, 1958); Land & Doig (branch-and-bound, 1960) |
| Typ≠ | Exact sequential optimization algorithm | Mathematical optimization |
| Původní zdroj≠ | Bellman, R. E. (1957). Dynamic Programming. Princeton University Press, Princeton, NJ. ISBN: 9780691079516 | Nemhauser, G. L., Wolsey, L. A. (1988). Integer and Combinatorial Optimization. Wiley-Interscience, New York. ISBN: 9780471359432 |
| Další názvy | DDP, Deterministic DP, Classical Dynamic Programming, Bellman Dynamic Programming | MIP, Mixed-Integer Linear Programming, MILP, Integer Programming |
| Příbuzné | 6 | 6 |
| Shrnutí≠ | 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. | Mixed-Integer Programming (MIP) is a mathematical optimization framework in which some decision variables must take integer values while others may be continuous. It generalizes linear programming and is widely used in operations research, logistics, scheduling, resource allocation, and engineering design, where indivisibility constraints — such as yes/no decisions or whole-unit quantities — arise naturally. |
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