A polynomial-time algorithm for a nonconvex chance-constrained program under the normal approximation

We study a chance-constrained optimization problem where the random variable appearing in the chance constraint follows a normal distribution whose mean and variance both depend linearly on the decision variables. Such structure may arise in many applications, including the normal approximation to t...

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Bibliographic Details
Published in:	Optimization letters Vol. 17; no. 2; pp. 265 - 282
Main Author:	Mildebrath, David
Format:	Journal Article
Language:	English
Published:	Berlin/Heidelberg Springer Berlin Heidelberg 01.03.2023
Subjects:	Computational Intelligence Mathematics Mathematics and Statistics Numerical and Computational Physics Operations Research/Decision Theory Optimization Original Paper Simulation Reverse convex programming Chance-constrained programming Polynomial-time algorithms Nonlinear optimization
ISSN:	1862-4472, 1862-4480
Online Access:	Get full text
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Summary:	We study a chance-constrained optimization problem where the random variable appearing in the chance constraint follows a normal distribution whose mean and variance both depend linearly on the decision variables. Such structure may arise in many applications, including the normal approximation to the Poisson distribution. We present a polynomial-time algorithm to solve the resulting nonconvex optimization problem, and illustrate the efficacy of our method using a numerical experiment.
ISSN:	1862-4472 1862-4480
DOI:	10.1007/s11590-022-01905-6