Alternating Optimization Algorithms for Joint Chance Constraint Optimization

Tao Yao
Pennsylvania State University

In this talk, we examine robust counterpart safe tractable approximation for joint chance constraint programs. A standard method to approximate a JCCP with a violation risk a is to decompose the JCC into m individual chance constraints using Boole’s inequality and arbitrarily assign each individual chance constraint a violation risk equal to a/m (Nemirovski and Shaprio 2006). In this talk, in order to derive less conservative optimal and safe tractable solution from the approximation, we propose alternating optimization algorithms to select better allocation of the violation risk among the individual chance constraints. The improvements of the solutions are theoretically guaranteed and numerical experiments are conducted to show the outperformance of the algorithms.

Nemirovski, A., A. Shapiro. 2006. Convex approximation of chance constrained programs. SIAM J. Optim. 17(4) 969-996.

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