Skip to main content
Article
A Simulation-based Approach to Solve a Specific Type of Chance Constrained Optimization
Stochastic Programming E-print Series
  • Lijian Chan, University of Dayton
Document Type
Article
Publication Date
2-1-2015
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
Place of Publication
Berlin, Germany
Peer Reviewed
Yes
Citation Information
Lijian Chan. "A Simulation-based Approach to Solve a Specific Type of Chance Constrained Optimization" Stochastic Programming E-print Series Vol. 2015 Iss. 1 (2015)
Available at: http://works.bepress.com/lijian_chen/8/