Stochastic dynamic programming: Difference between revisions
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Authors: Bo Yuan, Ali Amadeh, Max Greenberg, Raquel Sarabia Soto and Claudia Valero De la Flor (CHEME/SYSEN 6800, Fall 2021) | Authors: Bo Yuan, Ali Amadeh, Max Greenberg, Raquel Sarabia Soto and Claudia Valero De la Flor (CHEME/SYSEN 6800, Fall 2021) | ||
==Theory, methodology and algorithm discussion== | |||
===Theory=== | |||
Stochastic dynamic programming combines stochastic programming and dynamic programming. Therefore, to understand better what it is, it is better first to give two definitions: | |||
* '''Stochastic programming.''' Unlike in a deterministic problem, where a decision’s outcome is only determined by the decision itself and all the parameters are known, in stochastic programming there is uncertainty and the decision results in a distribution of transformations. | |||
* '''Dynamic programming.''' It is an optimization method that consists in dividing a complex problem into easier subprobems and solving them recursively to find the optimal sub-solutions which lead to the complex problem optima. | |||
Revision as of 20:20, 22 November 2021
Authors: Bo Yuan, Ali Amadeh, Max Greenberg, Raquel Sarabia Soto and Claudia Valero De la Flor (CHEME/SYSEN 6800, Fall 2021)
Theory, methodology and algorithm discussion
Theory
Stochastic dynamic programming combines stochastic programming and dynamic programming. Therefore, to understand better what it is, it is better first to give two definitions:
- Stochastic programming. Unlike in a deterministic problem, where a decision’s outcome is only determined by the decision itself and all the parameters are known, in stochastic programming there is uncertainty and the decision results in a distribution of transformations.
- Dynamic programming. It is an optimization method that consists in dividing a complex problem into easier subprobems and solving them recursively to find the optimal sub-solutions which lead to the complex problem optima.