Gradient trust region algorithm with limited memory BFGS update for nonsmooth convex minimization
AbstractBy means of a gradient strategy, the Moreau-Yosida regularization, limited memory BFGS update, and proximal method, we propose a trust-region method for nonsmooth convex minimization. The search direction is the combination of the gradient direction and the trust-region direction. The global convergence of this method is established under suitable conditions. Numerical results show that this method is competitive to other two methods. Copyright Springer Science+Business Media, LLC 2013
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Bibliographic InfoArticle provided by Springer in its journal Computational Optimization and Applications.
Volume (Year): 54 (2013)
Issue (Month): 1 (January)
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Web page: http://www.springer.com/math/journal/10589
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