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A limited memory quasi-Newton trust-region method for box constrained optimization.
- Source :
-
Journal of Computational & Applied Mathematics . Sep2016, Vol. 303, p105-118. 14p. - Publication Year :
- 2016
-
Abstract
- By means of Wolfe conditions strategy, we propose a quasi-Newton trust-region method to solve box constrained optimization problems. This method is an adequate combination of the compact limited memory BFGS and the trust-region direction while the generated point satisfies the Wolfe conditions and therefore maintains a positive-definite approximation to the Hessian of the objective function. The global convergence and the quadratic convergence of this method are established under suitable conditions. Finally, we compare our algorithms (IWTRAL and IBWTRAL) with an active set trust-region algorithm (ASTRAL) Xu and Burke (2007) on the CUTEst box constrained test problems presented by Gould et al. (2015). Numerical results show that the presented method is competitive and totally interesting for solving box constrained optimization. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 03770427
- Volume :
- 303
- Database :
- Academic Search Index
- Journal :
- Journal of Computational & Applied Mathematics
- Publication Type :
- Academic Journal
- Accession number :
- 114128883
- Full Text :
- https://doi.org/10.1016/j.cam.2016.02.026