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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

String-averaging incremental subgradients for constrained convex optimization with applications to reconstruction of tomographic images

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de Oliveira, Rafael Massambone ; Helou, Elias Salomao ; Costa, Eduardo Fontoura
Total Authors: 3
Document type: Journal article
Source: INVERSE PROBLEMS; v. 32, n. 11 NOV 2016.
Web of Science Citations: 3

We present a method for non-smooth convex minimization which is based on subgradient directions and string-averaging techniques. In this approach, the set of available data is split into sequences (strings) and a given iterate is processed independently along each string, possibly in parallel, by an incremental subgradient method. (ISM). The end-points of all strings are averaged to form the next iterate. The method is useful to solve sparse and large-scale non-smooth convex optimization problems, such as those arising in tomographic imaging. A convergence analysis is provided under realistic, standard conditions. Numerical tests are performed in a tomographic image reconstruction application, showing good performance for the convergence speed when measured as the decrease ratio of the objective function, in comparison to classical ISM. (AU)

FAPESP's process: 13/16508-3 - Fast computation of the generalized Backprojection operator with applications in tomographic image reconstruction
Grantee:Elias Salomão Helou Neto
Support type: Regular Research Grants
FAPESP's process: 13/07375-0 - CeMEAI - Center for Mathematical Sciences Applied to Industry
Grantee:José Alberto Cuminato
Support type: Research Grants - Research, Innovation and Dissemination Centers - RIDC
FAPESP's process: 15/10171-2 - Incremental methods and String-Averaging for non-differentiable convex optimization with inaccurate Subgradients
Grantee:Rafael Massambone de Oliveira
Support type: Scholarships in Brazil - Doctorate
FAPESP's process: 13/19380-8 - Control and filtering of stochastic systems
Grantee:Eduardo Fontoura Costa
Support type: Regular Research Grants