A Penalty Approach for Solving Generalized Absolute Value Equations

In this paper, we propose a penalty approach for solving generalized absolute value equations (GAVEs) of the type Ax−B|x|=b, (A,B∈Rn×n,b∈Rn). Firstly, we reformulate the GAVEs as variational inequality problems passing through an equivalent horizontal linear complementarity problem. To approximate the resulting variational inequality, a sequence of nonlinear equations containing a penalty term is then defined. Under a mild assumption, we show that the solution of the considered sequence converges to that of GAVE if the penalty parameter tends to infinity. An algorithm is developed where its corresponding theoretical arguments are well established. Finally, some numerical experiments are presented to show that our approach is quite appreciable.

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Publication Details

Journal
Axioms
Published
2025-06-22
DOI
https://doi.org/10.3390/axioms14070488
Primary Topic
Advanced Optimization Algorithms Research
Type
article
Field-Weighted Citation Impact
0.00
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article

A Penalty Approach for Solving Generalized Absolute Value Equations

Zahira Kebaili, Hassina Grar, Mohamed Achache
Axioms
Advanced Optimization Algorithms Research
article

A Penalty Approach for Solving Generalized Absolute Value Equations

Zahira Kebaili, Hassina Grar, Mohamed Achache
article en

Abstract

In this paper, we propose a penalty approach for solving generalized absolute value equations (GAVEs) of the type Ax−B|x|=b, (A,B∈Rn×n,b∈Rn). Firstly, we reformulate the GAVEs as variational inequality problems passing through an equivalent horizontal linear complementarity problem. To approximate the resulting variational inequality, a sequence of nonlinear equations containing a penalty term is then defined. Under a mild assumption, we show that the solution of the considered sequence converges to that of GAVE if the penalty parameter tends to infinity. An algorithm is developed where its corresponding theoretical arguments are well established. Finally, some numerical experiments are presented to show that our approach is quite appreciable.

AxiomsVol. 14(7)
University Ferhat Abbas of Setif (DZ)
Openalex Percentile: Top 77%
Advanced Optimization Algorithms Research
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