Nonsmooth Techniques for Computing Projected Solutions of Quasiequilibria via Gap Functions

Abstract Projected solutions to a quasiequilibrium problem allow overcoming the possible lack of solutions when the constraining set-valued map is not a self-map. This paper aims at providing a descent algorithm for computing projected solutions by relying on a reformulation of the problem as a nonsmooth optimization problem. The nonsmoothness of the gap function can be dealt with successfully through the nonexpansiveness of the projection and tools such as Clarke subdifferentials. Nonetheless, some additional difficulties arise since the projection brings in nonsmoothness also in constraints that are provided by differentiable bifunctions. Monotonicity assumptions on the constraints have to cope with this further issue both to devise the algorithm and prove its convergence. Preliminary numerical tests show a promising behaviour of the algorithm.

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Publication Details

Journal
Journal of Optimization Theory and Applications
Published
2025-10-25
DOI
https://doi.org/10.1007/s10957-025-02865-4
Primary Topic
Advanced Optimization Algorithms Research
Type
article
Field-Weighted Citation Impact
0.00

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article

Nonsmooth Techniques for Computing Projected Solutions of Quasiequilibria via Gap Functions

Giancarlo Bigi, Marco Castellani, Sara Latini
Journal of Optimization Theory and Applications
Advanced Optimization Algorithms Research
article

Nonsmooth Techniques for Computing Projected Solutions of Quasiequilibria via Gap Functions

Giancarlo Bigi, Marco Castellani, Sara Latini
article en

Abstract

Abstract Projected solutions to a quasiequilibrium problem allow overcoming the possible lack of solutions when the constraining set-valued map is not a self-map. This paper aims at providing a descent algorithm for computing projected solutions by relying on a reformulation of the problem as a nonsmooth optimization problem. The nonsmoothness of the gap function can be dealt with successfully through the nonexpansiveness of the projection and tools such as Clarke subdifferentials. Nonetheless, some additional difficulties arise since the projection brings in nonsmoothness also in constraints that are provided by differentiable bifunctions. Monotonicity assumptions on the constraints have to cope with this further issue both to devise the algorithm and prove its convergence. Preliminary numerical tests show a promising behaviour of the algorithm.

Journal of Optimization Theory and ApplicationsVol. 208(1)
University of Pisa (IT), University of L'Aquila (IT)
Istituto Nazionale di Alta Matematica "Francesco Severi", Università degli Studi dell'Aquila
Openalex Percentile: Top 55%
Advanced Optimization Algorithms Research
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