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：
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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.
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页码：615 / 635
页数：20
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