Relaxed-inertial proximal point type algorithms for quasiconvex minimization

被引：0

作者：

S.-M. Grad

F. Lara

R. T. Marcavillaca

机构：

[1] ENSTA Paris,Department of Applied Mathematics

[2] Polytechnic Institute of Paris,Departamento de Matemática, Facultad de Ciencias

[3] Corvinus Center for Operations Research,undefined

[4] Corvinus Institute for Advanced Studies,undefined

[5] Corvinus University of Budapest,undefined

[6] Universidad de Tarapacá,undefined

来源：

Journal of Global Optimization | 2023年 / 85卷

关键词：

Proximal point algorithms; Relaxed methods; Inertial methods; Generalized convexity; Strong quasiconvexity;

D O I：

暂无

中图分类号：

学科分类号：

摘要：

We propose a relaxed-inertial proximal point type algorithm for solving optimization problems consisting in minimizing strongly quasiconvex functions whose variables lie in finitely dimensional linear subspaces. A relaxed version of the method where the constraint set is only closed and convex is also discussed, and so is the case of a quasiconvex objective function. Numerical experiments illustrate the theoretical results.

引用

页码：615 / 635

页数：20

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