Video URL

https://simons.berkeley.edu/talks/tbd-240

Monte Carlo Sampling Approach to Solving Stochastic Multistage Programs

(2020). Monte Carlo Sampling Approach to Solving Stochastic Multistage Programs. The Simons Institute for the Theory of Computing. https://simons.berkeley.edu/talks/tbd-240

Monte Carlo Sampling Approach to Solving Stochastic Multistage Programs. The Simons Institute for the Theory of Computing, Dec. 01, 2020, https://simons.berkeley.edu/talks/tbd-240 

          @misc{ scivideos_16820,
            doi = {},
            url = {https://simons.berkeley.edu/talks/tbd-240},
            author = {},
            keywords = {},
            language = {en},
            title = {Monte Carlo Sampling Approach to Solving Stochastic Multistage Programs},
            publisher = {The Simons Institute for the Theory of Computing},
            year = {2020},
            month = {dec},
            note = {16820 see, \url{https://scivideos.org/Simons-Institute/16820}}
          }
Alex Shapiro (Georgia Tech)
Source Repository Simons Institute
Subject

Abstract

In this talk we discuss computational approaches based on Monte Carlo sampling techniques  to solving  multistage stochastic programming problems.  In some applications the considered programs have a periodical behavior. We demonstrate that in such cases it is possible to drastically reduce the number of stages by introducing a periodical analog of the so-called Bellman equations, used in Markov Decision Processes and Stochastic Optimal Control. Furthermore, we describe a primal - dual variant of the Stochastic Dual Dynamic Programming algorithm, applied to the constructed periodical Bellman equations. We consider risk neutral and risk averse settings.