Relaxed-Inertial Proximal Point Type Algorithms for Quasiconvex Minimization
Overview & Synopsis
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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Document Information
| Document Title | Relaxed-Inertial Proximal Point Type Algorithms for Quasiconvex Minimization |
|---|---|
| Author / Organization | Sorin-Mihai Grad, Felipe Lara, Raul Tintaya Marcavillaca |
| Subject & Discipline | Multidisciplinary Academic Open Research |
| Categories | Science & Mathematics |
| Publication Year | 2023 |
| License / Rights | Open Access / Creative Commons |
| Resource Verification | Listed PDF (Verification Pending) |
| Original Host Website |
HAL Open Science
(hal.science)
|
| File Format | application/pdf (Portable Document Format) |
| Language | EN |
| Catalog ID | DOC-72018 |
| Original Source Link | Visit Source Page on hal.science » |