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pro vyhledávání: '"Millan, Reinier Diaz"'
Autor:
Millán, Reinier Díaz, Ugon, Julien
In this paper we introduce two conceptual algorithms for minimising abstract convex functions. Both algorithms rely on solving a proximal-type subproblem with an abstract Bregman distance based proximal term. We prove their convergence when the set o
Externí odkaz:
http://arxiv.org/abs/2402.04281
Rational and neural network based approximations are efficient tools in modern approximation. These approaches are able to produce accurate approximations to nonsmooth and non-Lipschitz functions, including multivariate domain functions. In this pape
Externí odkaz:
http://arxiv.org/abs/2303.04436
Abstract convexity generalises classical convexity by considering the suprema of functions taken from an arbitrarily defined set of functions. These are called the abstract linear (abstract affine) functions. The purpose of this paper is to study the
Externí odkaz:
http://arxiv.org/abs/2206.02565
The theory of abstract convexity, also known as convexity without linearity, is an extension of the classical convex analysis. There are a number of remarkable results, mostly concerning duality, and some numerical methods, however, this area has not
Externí odkaz:
http://arxiv.org/abs/2202.09959
We present two approximate versions of the proximal subgradient method for minimizing the sum of two convex functions (not necessarily differentiable). The algorithms involve, at each iteration, inexact evaluations of the proximal operator and approx
Externí odkaz:
http://arxiv.org/abs/1805.10120
Autor:
Millán, Reinier Díaz
Publikováno v:
Biblioteca Digital de Teses e Dissertações da UFGUniversidade Federal de GoiásUFG.
Submitted by Erika Demachki (erikademachki@gmail.com) on 2015-05-21T19:19:51Z No. of bitstreams: 2 Tese - Reinier Díaz Millán - 2015.pdf: 3568052 bytes, checksum: b4c892f77911a368e1b8f629afb5e66e (MD5) license_rdf: 23148 bytes, checksum: 9da0b6dfac
Externí odkaz:
http://repositorio.bc.ufg.br/tede/handle/tede/4562
