MSS: MATLAB Software for L-BFGS Trust-Region Subproblems for Large-Scale Optimization. A MATLAB implementation of the Moré-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 Moré-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 im plementation 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.
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References in zbMATH (referenced in 6 articles , 1 standard article )
Showing results 1 to 6 of 6.
- Brust, Johannes J.; Marcia, Roummel F.; Petra, Cosmin G.: Large-scale quasi-Newton trust-region methods with low-dimensional linear equality constraints (2019)
- Brust, Johannes; Erway, Jennifer B.; Marcia, Roummel F.: On solving L-SR1 trust-region subproblems (2017)
- Burdakov, Oleg; Gong, Lujin; Zikrin, Spartak; Yuan, Ya-xiang: On efficiently combining limited-memory and trust-region techniques (2017)
- Erway, Jennifer B.; Marcia, Roummel F.: Algorithm 943: MSS: MATLAB software for L-BFGS trust-region subproblems for large-scale optimization (2014)
- Erway, Jennifer B.; Marcia, Roummel F.: Limited-memory BFGS systems with diagonal updates (2012)
- Moré, Jorge J.; Sorensen, D. C.: Computing a trust region step (1983)