Paper Info
Title
Semidefinite Programming For Chance Constrained Optimization Over Semialgebraic Sets.
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.
Year
DOI
Venue
2015
10.1137/140958736
SIAM JOURNAL ON OPTIMIZATION
Keywords
Field
DocType
semialgebraic set,chance constrained,SDP relaxation,augmented Lagrangian,first-order methods
Discrete mathematics,Semialgebraic set,Mathematical optimization,Regular polygon,Augmented Lagrangian method,Polynomial inequalities,Lagrangian relaxation,Mathematics,Semidefinite programming,Constrained optimization
Journal
Volume
Issue
ISSN
25
3
1052-6234
Citations 
PageRank 
References 
7
0.64
26
Authors
3
Name
Order
Citations
PageRank
Jasour, A.M.Z.1113.51
N. S. Aybat28910.49
Constantino M. Lagoa316425.38