Scenario-free stochastic programming with polynomial decision rules

Multi-stage stochastic programming provides a versatile framework for optimal decision making under uncertainty, but it gives rise to hard functional optimization problems since the adaptive recourse decisions must be modeled as functions of some or all uncertain parameters. We propose to approximate these recourse decisions by polynomial decision rules and show that the best polynomial decision rule of a fixed degree can be computed efficiently. We also show that the suboptimality of the best polynomial decision rule can be estimated efficiently by solving a dual version of the stochastic program in polynomial decision rules.


Published in:
IEEE Conference on Decision and Control and European Control Conference, 7806-7812
Presented at:
2011 50th IEEE Conference on Decision and Control and European Control Conference (CDC-ECC 2011), Orlando, FL, USA, December 12-15, 2011
Year:
2011
Publisher:
IEEE
ISBN:
978-1-61284-800-6
Laboratories:




 Record created 2014-01-29, last modified 2018-03-17

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