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Article Dans Une Revue Journal of Global Optimization Année : 2022

Relaxed-Inertial Proximal Point Type Algorithms for Quasiconvex Minimization

Résumé

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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Dates et versions

hal-03759660 , version 1 (24-08-2022)

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  • HAL Id : hal-03759660 , version 1

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Sorin-Mihai Grad, Felipe Lara, Raul Tintaya Marcavillaca. Relaxed-Inertial Proximal Point Type Algorithms for Quasiconvex Minimization. Journal of Global Optimization, inPress. ⟨hal-03759660⟩
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