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Stochastic Dynamic Programming — Sequential Decision-Making Under Uncertainty
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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Sources
- Bellman, R. (1957). Dynamic Programming. Princeton University Press, Princeton, NJ. ISBN: 9780486428093
- Puterman, M. L. (1994). Markov Decision Processes: Discrete Stochastic Dynamic Programming. John Wiley & Sons, New York. ISBN: 9780471619772
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Referenced by
Agent-based dynamic programmingBayesian Dynamic ProgrammingDeterministic Dynamic ProgrammingMulti-objective dynamic programmingMulti-objective Markov ModelPolicy Scenario Dynamic ProgrammingStochastic Integer ProgrammingStochastic Linear ProgrammingStochastic Markov ModelStochastic Mixed-Integer ProgrammingStochastic Multi-Objective OptimizationStochastic Scenario Analysis