Algorithm 943: MSS: MATLAB Software for L-BFGS Trust-Region Subproblems for Large-Scale Optimization

作者：Erway Jennifer B^*; Marcia Roummel F

来源：ACM Transactions on Mathematical Software, 2014, 40(4): 28.

DOI：10.1145/2616588

• Algorithms
• Design
• Large-scale unconstrained optimization
• trust-region methods
• limited-memory quasi-Newton methods
• L-BFGS

A MATLAB implementation of the More-Sorensen sequential (MSS) method is presented. The MSS method computes the minimizer of a quadratic function defined by a limited-memory BFGS matrix subject to a two-norm trust-region constraint. This solver is an adaptation of the More-Sorensen direct method into an L-BFGS setting for large-scale optimization. The MSS method makes use of a recently proposed stable fast direct method for solving large shifted BFGS systems of equations [Erway and Marcia 2012; Erway et al. 2012] and is able to compute solutions to any user-defined accuracy. This MATLAB implementation is a matrix-free iterative method for large-scale optimization. Numerical experiments on the CUTEr [Bongartz et al. 1995; Gould et al. 2003]) suggest that using the MSS method as a trust-region subproblem solver can require significantly fewer function and gradient evaluations needed by a trust-region method as compared with the Steihaug-Toint method.

• 出版日期2014-6