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Gradient trust region algorithm with limited memory BFGS update for nonsmooth convex minimization

Yuan, Gonglin; Wei, Zengxin; Wang, Zhongxing - In: Computational Optimization and Applications 54 (2013) 1, pp. 45-64

By 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...

Persistent link: https://www.econbiz.de/10010896513

On the local convergence of a derivative-free algorithm for least-squares minimization

Zhang, Hongchao; Conn, Andrew - In: Computational Optimization and Applications 51 (2012) 2, pp. 481-507

Persistent link: https://www.econbiz.de/10010539366

Reconstructing a matrix from a partial sampling of Pareto eigenvalues

Gajardo, Pedro; Seeger, Alberto - In: Computational Optimization and Applications 51 (2012) 3, pp. 1119-1135

Persistent link: https://www.econbiz.de/10010998268

Inexact Josephy–Newton framework for generalized equations and its applications to local analysis of Newtonian methods for constrained optimization

Izmailov, A.; Solodov, M. - In: Computational Optimization and Applications 46 (2010) 2, pp. 347-368

Persistent link: https://www.econbiz.de/10008533939

Examples of dual behaviour of Newton-type methods on optimization problems with degenerate constraints

Izmailov, A.; Solodov, M. - In: Computational Optimization and Applications 42 (2009) 2, pp. 231-264

Persistent link: https://www.econbiz.de/10005169145