Abstract
We discuss and develop the convex approximation for robust joint chance constraints under uncertainty of first- and second-order moments. Robust chance constraints are approximated by Worst-Case CVaR constraints which can be reformulated by a semidefinite programming. Then the chance constrained problem can be presented as semidefinite programming. We also find that the approximation for robust joint chance constraints has an equivalent individual quadratic approximation form.
Highlights
Chance constraints, called probabilistic constraints in the literature, have a long history in stochastic programming and are the direct way to treat stochastic data uncertainty
We discuss and develop the convex approximation for robust joint chance constraints under uncertainty of first- and secondorder moments
We find that the approximation for robust joint chance constraints has an equivalent individual quadratic approximation form
Summary
Called probabilistic constraints in the literature, have a long history in stochastic programming and are the direct way to treat stochastic data uncertainty. We discuss and develop the convex approximation for robust joint chance constraints under uncertainty of first- and secondorder moments. Robust chance constraints are approximated by Worst-Case CVaR constraints which can be reformulated by a semidefinite programming. We find that the approximation for robust joint chance constraints has an equivalent individual quadratic approximation form.
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