Document Type

Article

Publication Date

2-2015

Publication Source

Stochastic Programming E-print Series

Abstract

We solve the chance constrained optimization with convex feasible set through approximating the chance constraint by another convex smooth function. The approximation is based on the numerical properties of the Bernstein polynomial that is capable of effectively controlling the approximation error for both function value and gradient. Thus, we adopt a first-order algorithm to reach a satisfactory solution which is expected to be optimal. When the explicit expression of joint distribution is not available, we then use Monte Carlo approach to numerically evaluate the chance constraint to obtain an optimal solution by probability. Numerical results for known problem instances are presented.

Document Version

Preprint

Comments

The document available for download is the pre-reviewed and pre-edited author's accepted manuscript, provided in compliance with the publisher's policy on self-archiving. The version of record may contain differences that have come about after the copy editing and layout processes.

Permission documentation is on file.

Publisher

Stochastic Programming Society

Volume

2015

Issue

1

Place of Publication

Berlin, Germany

Peer Reviewed

yes