Zobrazeno 1 - 10
of 80 795
pro vyhledávání: '"A. Bregman"'
Autor:
Savchuk, O. S., Alkousa, M. S., Shushko, A. S., Vyguzov, A. A., Stonyakin, F. S., Pasechnyuk, D. A., Gasnikov, A. V.
In this paper, we propose some accelerated methods for solving optimization problems under the condition of relatively smooth and relatively Lipschitz continuous functions with an inexact oracle. We consider the problem of minimizing the convex diffe
Externí odkaz:
http://arxiv.org/abs/2411.16743
Autor:
Achab, Mastane
We present a generalization of the proximal operator defined through a convex combination of convex objectives, where the coefficients are updated in a minimax fashion. We prove that this new operator is Bregman firmly nonexpansive with respect to a
Externí odkaz:
http://arxiv.org/abs/2411.00928
Autor:
Nielsen, Frank
Publikováno v:
Entropy 2024, 26(12), 1008
The symmetric Kullback-Leibler centroid also called the Jeffreys centroid of a set of mutually absolutely continuous probability distributions on a measure space provides a notion of centrality which has proven useful in many tasks including informat
Externí odkaz:
http://arxiv.org/abs/2410.14326
We study optimal payoff choice for an expected utility maximizer under the constraint that their payoff is not allowed to deviate ``too much'' from a given benchmark. We solve this problem when the deviation is assessed via a Bregman-Wasserstein (BW)
Externí odkaz:
http://arxiv.org/abs/2411.18397
Autor:
Gilles, Jerome, Osher, Stanley
In this paper, we design a very simple algorithm based on Split Bregman iterations to numerically solve the cartoon + textures decomposition model of Meyer. This results in a significant gain in speed compared to Chambolle's nonlinear projectors.
Externí odkaz:
http://arxiv.org/abs/2410.22777
Autor:
Alsubhi, Abdulmajeed, Renaut, Rosemary
We consider the solution of the $\ell_1$ regularized image deblurring problem using isotropic and anisotropic regularization implemented with the split Bregman algorithm. For large scale problems, we replace the system matrix $A$ using a Kronecker pr
Externí odkaz:
http://arxiv.org/abs/2410.00233
Bilevel optimization methods are increasingly relevant within machine learning, especially for tasks such as hyperparameter optimization and meta-learning. Compared to the offline setting, online bilevel optimization (OBO) offers a more dynamic frame
Externí odkaz:
http://arxiv.org/abs/2409.10470
Imaging tasks are typically tackled using a structured optimization framework. This paper delves into a class of algorithms for difference-of-convex (DC) structured optimization, focusing on minimizing a DC function along with a possibly nonconvex fu
Externí odkaz:
http://arxiv.org/abs/2409.03262
Autor:
Wolf, Tobias, Driggs, Derek, Papafitsoros, Kostas, Resmerita, Elena, Schönlieb, Carola-Bibiane
We consider the task of image reconstruction while simultaneously decomposing the reconstructed image into components with different features. A commonly used tool for this is a variational approach with an infimal convolution of appropriate function
Externí odkaz:
http://arxiv.org/abs/2409.01097