Please use this identifier to cite or link to this item: http://hdl.handle.net/10316/45702
Title: Globally convergent DC trust-region methods
Authors: Le Thi, Hoai An 
Huynh, Van Ngai 
Dinh, Tao Pham 
Vaz, A. Ismael F. 
Vicente, Luís Nunes 
Issue Date: 2014
Publisher: Springer US
Project: PEst-C/MAT/UI0324/2011 
Serial title, monograph or event: Journal of Global Optimization
Volume: 59
Issue: 2-3
Abstract: In this paper, we investigate the use of DC (Difference of Convex functions) models and algorithms in the application of trust-region methods to the solution of a class of nonlinear optimization problems where the constrained set is closed and convex (and, from a practical point of view, where projecting onto the feasible region is computationally affordable). We consider DC local models for the quadratic model of the objective function used to compute the trust-region step, and apply a primal-dual subgradient method to the solution of the corresponding trust-region subproblems. One is able to prove that the resulting scheme is globally convergent to first-order stationary points. The theory requires the use of exact second-order derivatives but, in turn, the computation of the trust-region step asks only for one projection onto the feasible region (in comparison to the calculation of the generalized Cauchy point which may require more). The numerical efficiency and robustness of the proposed new scheme when applied to bound-constrained problems is measured by comparing its performance against some of the current state-of-the-art nonlinear programming solvers on a vast collection of test problems.
URI: http://hdl.handle.net/10316/45702
DOI: 10.1007/s10898-014-0170-6
Rights: embargoedAccess
Appears in Collections:I&D CMUC - Artigos em Revistas Internacionais

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