학술논문

Relaxed-Inertial Proximal Point Algorithms for Nonconvex Equilibrium Problems with Applications
Document Type
Original Paper
Source
Journal of Optimization Theory and Applications. :1-30
Subject
Proximal point algorithms
Inertial algorithms
Equilibrium problems
Nonconvex optimization
Quasiconvexity
Language
English
ISSN
0022-3239
1573-2878
Abstract
We propose a relaxed-inertial proximal point algorithm for solving equilibrium problems involving bifunctions which satisfy in the second variable a generalized convexity notion called strong quasiconvexity, introduced by Polyak (Sov Math Dokl 7:72–75, 1966). The method is suitable for solving mixed variational inequalities and inverse mixed variational inequalities involving strongly quasiconvex functions, as these can be written as special cases of equilibrium problems. Numerical experiments where the performance of the proposed algorithm outperforms one of the standard proximal point methods are provided, too.