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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.)

epsilon-subgradient algorithms for bilevel convex optimization

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Helou, Elias S. ; Simoes, Lucas E. A.
Total Authors: 2
Document type: Journal article
Source: INVERSE PROBLEMS; v. 33, n. 5 MAY 2017.
Web of Science Citations: 1

This paper introduces and studies the convergence properties of a new class of explicit e-subgradient methods for the task of minimizing a convex function over a set of minimizers of another convex minimization problem. The general algorithm specializes to some important cases, such as first-order methods applied to a varying objective function, which have computationally cheap iterations. We present numerical experimentation concerning certain applications where the theoretical framework encompasses efficient algorithmic techniques, enabling the use of the resulting methods to solve very large practical problems arising in tomographic image reconstruction. (AU)

FAPESP's process: 13/14615-7 - On the nonmonotone line search in gradient sampling methods for nonconvex and nonsmooth optimization
Grantee:Lucas Eduardo Azevedo Simões
Support type: Scholarships in Brazil - Doctorate
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: 11/02219-4 - New incremental methods for bi-level non-differentiable convex optimization with applications to image reconstruction for emission tomography
Grantee:Lucas Eduardo Azevedo Simões
Support type: Scholarships in Brazil - Master
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