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Methods in optimization and feasibility for inverse problems and tomography

Grant number: 13/19504-9
Support type:Scholarships in Brazil - Post-Doctorate
Effective date (Start): February 01, 2014
Effective date (End): January 31, 2016
Field of knowledge:Physical Sciences and Mathematics - Mathematics - Applied Mathematics
Principal researcher:Alvaro Rodolfo de Pierro
Grantee:Daniel Reem
Home Institution: Instituto de Ciências Matemáticas e de Computação (ICMC). Universidade de São Paulo (USP). São Carlos , SP, Brazil

Abstract

Many Inverse Problems, especially in emission and X rays computed tomography, are modeled by large scale systems of linear equations that give rise to ill conditioned mathematical problems. In order to find a stable solution it is necessary to add information not contained in the equations. All this information is used through different models where either an optimization or a feasibility problem should be solved. Also, this generates the necessity of studying specific algorithms for this type of mathematical problems as well as the study of the parameters appearing in those algorithms. In this project we propose to study several open problems and theoretical aspects of these algorithms and models. We will study the relationship between the Douglas-Rachford method and relaxed subgradient as well as the acceleration proposed by Nesterov-Beck-Teboulle. Also we will study the possibility of eliminating the underrelaxation parameters analyzing the asymptotic convergence properties. We will analyze the possibility of obtaining a unified theory for the set of divergence measures, like Kullback-Leibler, that are used to measure equations consistency that models the information about noise in data in Inverse Problems. (AU)

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Scientific publications
(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)
REEM, DANIEL; REICH, SIMEON; DE PIERRO, ALVARO. A Telescopic Bregmanian Proximal Gradient Method Without the Global Lipschitz Continuity Assumption. JOURNAL OF OPTIMIZATION THEORY AND APPLICATIONS, v. 182, n. 3, p. 851-884, SEP 2019. Web of Science Citations: 0.
REEM, DANIEL; REICH, SIMEON; DE PIERRO, ALVARO. Re-examination of Bregman functions and new properties of their divergences. OPTIMIZATION, v. 68, n. 1, SI, p. 279-348, JAN 2 2019. Web of Science Citations: 3.
REEM, DANIEL; DE PIERRO, ALVARO. A new convergence analysis and perturbation resilience of some accelerated proximal forward-backward algorithms with errors. INVERSE PROBLEMS, v. 33, n. 4 APR 2017. Web of Science Citations: 6.

Please report errors in scientific publications list by writing to: cdi@fapesp.br.