Year: 2003
Author: Wen-Yu Sun, Raimundo J. B. de Sampaio, M. A. B. Candido
Journal of Computational Mathematics, Vol. 21 (2003), Iss. 4 : pp. 451–462
Abstract
In this paper we present some algorithms for minimization of DC function (difference of two convex functions). They are descent methods of the proximal-type which use the convex properties of the two convex functions separately. We also consider an approximate proximal point algorithm. Some properties of the $\epsilon$-subdifferential and the $\epsilon$-directional derivative are discussed. The convergence properties of the algorithms are established in both exact and approximate forms. Finally, we give some applications to the concave programming and maximum eigenvalue problems.
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Journal Article Details
Publisher Name: Global Science Press
Language: English
DOI: https://doi.org/2003-JCM-10248
Journal of Computational Mathematics, Vol. 21 (2003), Iss. 4 : pp. 451–462
Published online: 2003-01
AMS Subject Headings:
Copyright: COPYRIGHT: © Global Science Press
Pages: 12
Keywords: Nonconvex optimization Nonsmooth optimization DC function Proximal point algorithm $\epsilon$-subgradient.