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MSS: MATLAB software for L-BFGS trust-region subproblems for large-scale optimization

Jennifer Erway (erwayjb***at***wfu.edu)
Roummel Marcia (rmarcia***at***ucmerced.edu)

Abstract: 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 [13, 12] 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 [3, 16]) 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.

Keywords: Large-scale unconstrained optimization, trust-region methods, limited-memory quasi-Newton methods, L-BFGS

Category 1: Nonlinear Optimization

Citation: Technical Report 2012-5, Department of Mathematics, Wake Forest University, 2012.

Download: [PDF]

Entry Submitted: 12/02/2012
Entry Accepted: 12/02/2012
Entry Last Modified: 07/15/2013

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