A polynomial optimization framework for polynomial quasi-variational inequalities with Moment-SOS relaxations

We consider quasi-variational inequality problems (QVI) given by polynomial functions. By applying Lagrange multiplier expressions, we formulate polynomial optimization problems whose minimizers are KKT points for the QVI. Then, feasible extensions are exploited to preclude KKT points that are not solutions. Moment-SOS relaxations are incorporated to solve the polynomial optimization problems in our methods. Under certain conditions, our approach guarantees to find a solution to the QVI or detect the nonexistence of solutions.

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Publication Details

Journal
Numerical Algebra Control and Optimization
Published
2024-12-10
DOI
https://doi.org/10.3934/naco.2024054
Primary Topic
Advanced Optimization Algorithms Research
Type
article
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article

A polynomial optimization framework for polynomial quasi-variational inequalities with Moment-SOS relaxations

Xindong Tang, Wenhu Zhong, Min Zhang
Numerical Algebra Control and Optimization
Advanced Optimization Algorithms Research
article

A polynomial optimization framework for polynomial quasi-variational inequalities with Moment-SOS relaxations

Xindong Tang, Wenhu Zhong, Min Zhang
article en

Abstract

We consider quasi-variational inequality problems (QVI) given by polynomial functions. By applying Lagrange multiplier expressions, we formulate polynomial optimization problems whose minimizers are KKT points for the QVI. Then, feasible extensions are exploited to preclude KKT points that are not solutions. Moment-SOS relaxations are incorporated to solve the polynomial optimization problems in our methods. Under certain conditions, our approach guarantees to find a solution to the QVI or detect the nonexistence of solutions.

Numerical Algebra Control and OptimizationVol. 16(0)
Guangzhou University (CN), University of Bath (GB)
Reduced inequalities
Openalex Percentile: Top 65%
Advanced Optimization Algorithms Research
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