  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.
