Published by the Foundation for Open Access Statistics Editors-in-chief: Bettina Grün, Torsten Hothorn, Rebecca Killick, Edzer Pebesma, Achim Zeileis    ISSN 1548-7660; CODEN JSSOBK
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Authors: Ernesto G. Birgin, Jose Mario Martínez, Marcos Raydan
Title: Spectral Projected Gradient Methods: Review and Perspectives
Abstract: Over the last two decades, it has been observed that using the gradient vector as a search direction in large-scale optimization may lead to efficient algorithms. The effectiveness relies on choosing the step lengths according to novel ideas that are related to the spectrum of the underlying local Hessian rather than related to the standard decrease in the objective function. A review of these so-called spectral projected gradient methods for convex constrained optimization is presented. To illustrate the performance of these low-cost schemes, an optimization problem on the set of positive definite matrices is described.

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Paper: Spectral Projected Gradient Methods: Review and Perspectives     Download PDF (Downloads: 5514)
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DOI: 10.18637/jss.v060.i03

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Code: GNU General Public License (at least one of version 2 or version 3) or a GPL-compatible license.