A limited memory quasi-Newton trust-region method for box constrained optimization
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摘要
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.
论文关键词:Constrained optimization,Limited memory quasi-Newton,Line-search,Wolfe conditions,Trust-region framework,Theoretical convergence
论文评审过程:Received 27 August 2015, Revised 8 January 2016, Available online 3 March 2016, Version of Record 16 March 2016.
论文官网地址:https://doi.org/10.1016/j.cam.2016.02.026