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Hauptverfasser: Huang, Lei, Nie, Jiawang, Wang, Jiajia
Format: Preprint
Veröffentlicht: 2025
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Online-Zugang:https://arxiv.org/abs/2504.00257
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author Huang, Lei
Nie, Jiawang
Wang, Jiajia
author_facet Huang, Lei
Nie, Jiawang
Wang, Jiajia
contents We study the optimization problem over the weakly Pareto set of a convex multiobjective optimization problem given by polynomial functions. Using Lagrange multiplier expressions and the weight vector, we give three types of representations for the weakly Pareto set. Using these representations, we reformulate the optimization problem over the weakly Pareto set as a polynomial optimization problem. We then apply the Moment--SOS hierarchy to solve it and analyze its convergence properties under certain conditions. Numerical experiments are provided to demonstrate the effectiveness of our methods. Applications in multi-task learning are also presented.
format Preprint
id arxiv_https___arxiv_org_abs_2504_00257
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Optimization over the weakly Pareto set and multi-task learning
Huang, Lei
Nie, Jiawang
Wang, Jiajia
Optimization and Control
We study the optimization problem over the weakly Pareto set of a convex multiobjective optimization problem given by polynomial functions. Using Lagrange multiplier expressions and the weight vector, we give three types of representations for the weakly Pareto set. Using these representations, we reformulate the optimization problem over the weakly Pareto set as a polynomial optimization problem. We then apply the Moment--SOS hierarchy to solve it and analyze its convergence properties under certain conditions. Numerical experiments are provided to demonstrate the effectiveness of our methods. Applications in multi-task learning are also presented.
title Optimization over the weakly Pareto set and multi-task learning
topic Optimization and Control
url https://arxiv.org/abs/2504.00257