The majorization minimization algorithm for solving nonconvex generalized Nash equilibrium problems

This paper proposes a majorization minimization (MM) algorithm for nonconvex generalized Nash equilibrium problems. The algorithm initially utilizes an augmented Lagrangian function to convert the original problem into an unconstrained optimization problem. Subsequently, by integrating MM techniques...

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Veröffentlicht in:International journal of computer mathematics Jg. 102; H. 8; S. 1112 - 1129
Hauptverfasser: Shi, Yingao, Yu, Zhensheng
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Taylor & Francis 03.08.2025
Schlagworte:
augmented Lagrangian function
generalized Nash equilibrium
global convergence
majorization minimization
Nonconvex
ISSN:0020-7160, 1029-0265
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Zusammenfassung:This paper proposes a majorization minimization (MM) algorithm for nonconvex generalized Nash equilibrium problems. The algorithm initially utilizes an augmented Lagrangian function to convert the original problem into an unconstrained optimization problem. Subsequently, by integrating MM techniques, a suitable convex surrogate function is devised to develop a new algorithm. Under relatively broad assumptions, the global convergence of the algorithm has been proven, and its effectiveness and superiority have been verified through numerical experiments.
ISSN:0020-7160
1029-0265
DOI:10.1080/00207160.2025.2475054