Polynomial optimization relaxations for generalized semi-infinite programs

Abstract This paper studies generalized semi-infinite programs (GSIPs) given by polynomials. We propose a hierarchy of polynomial optimization relaxations to solve them. They are based on Lagrange multiplier expressions and polynomial extensions. Moment-SOS relaxations are applied to solve the polynomial optimization. The convergence of this hierarchy is shown under certain conditions. In particular, the classical semi-infinite programs can be solved as a special case of GSIPs. We also study GSIPs that have convex infinity constraints and show that they can be solved exactly by a single polynomial optimization relaxation. The computational efficiency is demonstrated by extensive numerical results.

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Publication Details

Journal
Mathematical Programming Computation
Published
2025-04-18
DOI
https://doi.org/10.1007/s12532-025-00280-4
Citations
1
Primary Topic
Advanced Optimization Algorithms Research
Type
article
Field-Weighted Citation Impact
2.63

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article

Polynomial optimization relaxations for generalized semi-infinite programs

Jiawang Nie, Xiaomeng Hu
1 citations
Mathematical Programming Computation
Advanced Optimization Algorithms Research
2.63
article

Polynomial optimization relaxations for generalized semi-infinite programs

Jiawang Nie, Xiaomeng Hu
article en
1 citations

Abstract

Abstract This paper studies generalized semi-infinite programs (GSIPs) given by polynomials. We propose a hierarchy of polynomial optimization relaxations to solve them. They are based on Lagrange multiplier expressions and polynomial extensions. Moment-SOS relaxations are applied to solve the polynomial optimization. The convergence of this hierarchy is shown under certain conditions. In particular, the classical semi-infinite programs can be solved as a special case of GSIPs. We also study GSIPs that have convex infinity constraints and show that they can be solved exactly by a single polynomial optimization relaxation. The computational efficiency is demonstrated by extensive numerical results.

Mathematical Programming ComputationVol. 17(3)
University of California San Diego (US)
National Science Foundation
Openalex Percentile: Top 15%
Advanced Optimization Algorithms Research
2.63
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