Print Email Facebook Twitter Accelerating the LSTRS algorithm Title Accelerating the LSTRS algorithm Author Lampe, J. Rojas, M. Sorensen, D.C. Voss, H. Faculty Electrical Engineering, Mathematics and Computer Science Date 2010-07-31 Abstract The LSTRS software for the efficient solution of the Large-Scale Trust-Region Subproblem was proposed in [Rojas, Santos, Sorensen: ACM ToMS 34 (2008), Article 11]. The LSTRS method is based on recasting the problem in terms of a parameter-dependent eigenvalue problem and adjusting the parameter iteratively. The essential work at each iteration is the solution of an eigenvalue problem for the smallest eigenvalue of a bordered Hessian matrix (or two smallest eigenvalues in the potential hard case) and associated eigenvector(s). Using the Nonlinear Arnoldi method to solve the eigenvalue problems makes it possible to recycle most of the information from previous iterations which can substantially accelerate LSTRS. Subject constrained quadratic optimizationregularizationtrust-regionARPACKNonlinear Arnoldi method To reference this document use: http://resolver.tudelft.nl/uuid:bf21a632-55be-4579-be53-0f4566d08775 Publisher Delft University of Technology, Faculty of Electrical Engineering, Mathematics and Computer Science, Delft Institute of Applied Mathematics ISSN 1389-6520 Source Reports of the Department of Applied Mathematical Analysis, 10-15 Part of collection Institutional Repository Document type report Rights (c)2010 Lampe, J., Rojas, M., Sorensen, D.C., Voss, H. Files PDF 10.15.Lampe.rev.ed.pdf 668.35 KB Close viewer /islandora/object/uuid:bf21a632-55be-4579-be53-0f4566d08775/datastream/OBJ/view