Advanced search
Start date
Betweenand
Related content
(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

INCREMENTAL SUBGRADIENTS FOR CONSTRAINED CONVEX OPTIMIZATION: A UNIFIED FRAMEWORK AND NEW METHODS

Full text
Author(s):
Helou Neto, Elias Salomao [1] ; De Pierro, Alvaro Rodolfo [1]
Total Authors: 2
Affiliation:
[1] Univ Estadual Campinas, Dept Appl Math, BR-13081970 Campinas, SP - Brazil
Total Affiliations: 1
Document type: Journal article
Source: SIAM JOURNAL ON OPTIMIZATION; v. 20, n. 3, p. 1547-1572, 2009.
Web of Science Citations: 23
Abstract

We present a unifying framework for nonsmooth convex minimization bringing together is an element of-subgradient algorithms and methods for the convex feasibility problem. This development is a natural step for is an element of-subgradient methods in the direction of constrained optimization since the Euclidean projection frequently required in such methods is replaced by an approximate projection, which is often easier to compute. The developments are applied to incremental subgradient methods, resulting in new algorithms suitable to large-scale optimization problems, such as those arising in tomographic imaging. (AU)

FAPESP's process: 02/07153-2 - Algorithms for tomographic reconstruction: optimization, restoration, quantification and clinical application
Grantee:Sergio Shiguemi Furuie
Support Opportunities: Research Projects - Thematic Grants