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A New Extragradient Method for Strongly Pseudomonotone Variational Inequalities
(Taylor & Francis, 2016)
This article proposes a new extragradient solution method for strongly pseudomonotone variational inequalities. A detailed analysis of the iterative sequences' convergence and of the range of applicability of the method ...
Extragradient method in optimization: convergence and complexity
(2018)
We consider the extragradient method to minimize the sum of two functions, the first one being smooth and the second being convex. Under the Kurdyka-Aojasiewicz assumption, we prove that the sequence produced by the ...
Hybrid extragradient proximal algorithm coupled with parametric approximation and penalty/barrier methods
(TAYLOR & FRANCIS LTD., 2013)
Extragradient method with variance reduction for stochastic variational inequalities
(SIAM Publications, 2017)
We propose an extragradient method with stepsizes bounded away from zero for stochastic variational inequalities requiring only pseudomonotonicity. We provide convergence and complexity analysis, allowing for an unbounded ...
On the Spingarn's partial inverse method: inexact versions, convergence rates and applications to operator splitting and optimization
(2018)
Neste trabalho, propomos e estudamos a complexidade computacional (em número de iterações) de uma versão inexata do método das inversas parciais de Spingarn. Os principais resultados de complexidade são obtidos através ...
Relative-error inexact versions of Douglas-Rachford and ADMM splitting algorithms
(2020)
Nesta tese, propomos e analisamos novas versões do método Douglas-Rachford splitting (DRS) para operadores monótonos maximais e do alternating direction method of multipliers (ADMM) para otimização convexa. Inicialmente, ...
On relative-error inertial-relaxed inexact proximal algorithms for monotone inclusion problems
(2020)
Neste trabalho, propomos e estudamos uma versão inercial subrelaxada e com erro relativo do método proximal extragradiente (HPE) de Sodolov e Svaiter para resolver problemas de inclusão monótono. Estudamos a convergência ...
Análisis de modelos de difusión por imágenes de resonancia magnética nuclear con machine learning
(Universidad Nacional de ColombiaBogotá - Ciencias - Maestría en Física MédicaDepartamento de FísicaFacultad de CienciasBogotá, ColombiaUniversidad Nacional de Colombia - Sede Bogotá, 2022-05)
En este trabajo se presenta la caracterización de las curvas de atenuación por difusión vóxel a vóxel de 4 conjuntos de imágenes (uno de próstata, dos de cerebro humano y uno de cerebro ex vivo de una neoplasia benigna). ...