Semidefinite programming for chance constrained optimization over semialgebraic sets

A. M. Jasour, N. S. Aybat, C. M. Lagoa

Research output: Contribution to journalArticle

15 Scopus citations

Abstract

In this paper, "chance optimization" problems are introduced, where one aims at maximizing the probability of a set defined by polynomial inequalities. These problems are, in general, nonconvex and computationally hard. With the objective of developing systematic numerical procedures to solve such problems, a sequence of convex relaxations based on the theory of measures and moments is provided, whose sequence of optimal values is shown to converge to the optimal value of the original problem. Indeed, we provide a sequence of semidefinite programs of increasing dimension which can arbitrarily approximate the solution of the original problem. To be able to efficiently solve the resulting large-scale semidefinite relaxations, a first-order augmented Lagrangian algorithm is implemented. Numerical examples are presented to illustrate the computational performance of the proposed approach.

Original languageEnglish (US)
Pages (from-to)1411-1440
Number of pages30
JournalSIAM Journal on Optimization
Volume25
Issue number3
DOIs
StatePublished - 2015

All Science Journal Classification (ASJC) codes

  • Software
  • Theoretical Computer Science

Fingerprint Dive into the research topics of 'Semidefinite programming for chance constrained optimization over semialgebraic sets'. Together they form a unique fingerprint.

  • Cite this