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pro vyhledávání: '"Bento, A. C."'
This paper develops the novel convergence analysis of a generic class of descent methods in nonsmooth and nonconvex optimization under several versions of the Kurdyka-\L ojasiewicz (KL) property. Along other results, we prove the finite termination o
Externí odkaz:
http://arxiv.org/abs/2407.00812
This paper is devoted to general nonconvex problems of multiobjective optimization in Hilbert spaces. Based on Mordukhovich's limiting subgradients, we define a new notion of Pareto critical points for such problems, establish necessary optimality co
Externí odkaz:
http://arxiv.org/abs/2403.09922
Autor:
Alba, Henry D.R., de Freitas, José E., Júnior, Leite, Laudí C., Azevêdo, José A.G., Santos, Stefanie A., Pina, Douglas dos S., Cirne, Luis G.A., Rodrigues, Carlindo S., Tosto, Manuela S.L., Bento, Silvia C., Grimaldi, Amanda B., de Carvalho, Gleidson G.P.
Publikováno v:
In Livestock Science August 2024 286
In this paper, is introduced a new proposal of resolvent for equilibrium problems in terms of the Busemann's function. A great advantage of this new proposal is that, in addition to be a natural extension of the proposal in the linear setting by Comb
Externí odkaz:
http://arxiv.org/abs/2107.02223
In this paper we present an inexact proximal point method for variational inequality problem on Hadamard manifolds and study its convergence properties. The proposed algorithm is inexact in two sense. First, each proximal subproblem is approximated b
Externí odkaz:
http://arxiv.org/abs/2103.02116
Akademický článek
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The aim of this paper is to present an extragradient method for variational inequality associated to a point-to-set vector field in Hadamard manifolds and to study its convergence properties. In order to present our method the concept of $\epsilon$-e
Externí odkaz:
http://arxiv.org/abs/1804.09292
Publikováno v:
Journal of Aging & Physical Activity; Aug2023, Vol. 31 Issue 4, p693-704, 12p, 5 Charts, 5 Graphs
This paper considers optimization problems on Riemannian manifolds and analyzes iteration-complexity for gradient and subgradient methods on manifolds with non-negative curvature. By using tools from the Riemannian convex analysis and exploring direc
Externí odkaz:
http://arxiv.org/abs/1609.04869
In this paper, we extend the proximal point algorithm for vector optimization from the Euclidean space to the Riemannian context. Under suitable assumptions on the objective function the well definition and full convergence of the method to a weak ef
Externí odkaz:
http://arxiv.org/abs/1512.06081