Interior Proximal Algorithm with Variable Metric for Second-Order Cone Programming: Applications to Structural Optimization and Support Vector Machines
Author
dc.contributor.author
Álvarez Daziano, Felipe
Author
dc.contributor.author
López Luis, Julio
Author
dc.contributor.author
Ramírez Cabrera, Héctor
Admission date
dc.date.accessioned
2016-12-06T20:08:50Z
Available date
dc.date.available
2016-12-06T20:08:50Z
Publication date
dc.date.issued
2009-10
Cita de ítem
dc.identifier.citation
Optimization Methods and Software Vol. 00, No. 00, October 2009, 1–23
es_ES
Identifier
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10.1080/10556780903483356
Identifier
dc.identifier.uri
https://repositorio.uchile.cl/handle/2250/141711
Abstract
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In this work, we propose an inexact interior proximal-type algorithm for solving convex second-order cone programs. This kind of problem consists of minimizing a convex function (possibly nonsmooth) over the intersection of an affine linear space with the Cartesian product of second-order cones. The proposed algorithm uses a variable metric, which is induced by a class of positive-definite matrices and an appropriate choice of regularization parameter. This choice ensures the well definedness of the proximal algorithm and forces the iterates to belong to the interior of the feasible set. Also, under suitable assumptions, it is proven that each limit point of the sequence generated by the algorithm solves the problem. Finally, computational results applied to structural optimization and support vector machines are presented
Interior Proximal Algorithm with Variable Metric for Second-Order Cone Programming: Applications to Structural Optimization and Support Vector Machines