LSTRS

Algorithm 873: LSTRS: MATLAB software for large-scale trust-region subproblems and regularization A MATLAB 6.0 implementation of the LSTRS method is presented. LSTRS was described in Rojas et al. [2000]. LSTRS is designed for large-scale quadratic problems with one norm constraint. The method is based on a reformulation of the trust-region subproblem as a parameterized eigenvalue problem, and consists of an iterative procedure that finds the optimal value for the parameter. The adjustment of the parameter requires the solution of a large-scale eigenvalue problem at each step. LSTRS relies on matrix-vector products only and has low and fixed storage requirements, features that make it suitable for large-scale computations. In the MATLAB implementation, the Hessian matrix of the quadratic objective function can be specified either explicitly, or in the form of a matrix-vector multiplication routine. Therefore, the implementation preserves the matrix-free nature of the method. A description of the LSTRS method and of the MATLAB software, version 1.2, is presented. Comparisons with other techniques and applications of the method are also included. A guide for using the software and examples are provided.

This software is also peer reviewed by journal TOMS.


References in zbMATH (referenced in 34 articles )

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  1. Gratton, Serge; Gürol, Selime; Toint, Philippe L.: Preconditioning and globalizing conjugate gradients in dual space for quadratically penalized nonlinear-least squares problems (2013)
  2. Martin, David R.; Reichel, Lothar: Minimization of functionals on the solution of a large-scale discrete ill-posed problem (2013)
  3. Benner, Peter; Embree, Mark; Lehoucq, Richard B.; Kelley, C. T.: A mathematical biography of Danny C. Sorensen (2012)
  4. Landi, G.; Loli Piccolomini, E.: A feasible direction method for image restoration (2012)
  5. Lampe, J.; Rojas, M.; Sorensen, D. C.; Voss, H.: Accelerating the LSTRS algorithm (2011)
  6. Li, Qingna; Qi, Houduo; Xiu, Naihua: Block relaxation and majorization methods for the nearest correlation matrix with factor structure (2011)
  7. Loli Piccolomini, E.; Zama, F.: An iterative algorithm for large size least-squares constrained regularization problems (2011)
  8. Lampe, Jörg; Voss, Heinrich: Solving regularized total least squares problems based on eigenproblems (2010)
  9. Erway, Jennifer B.; Gill, Philip E.; Griffin, Joshua D.: Iterative methods for finding a trust-region step (2009)
  10. Apostolopoulou, M. S.; Sotiropoulos, D. G.; Pintelas, P.: Solving the quadratic trust-region subproblem in a low-memory BFGS framework (2008)
  11. Rojas, Marielba; Santos, Sandra A.; Sorensen, Danny C.: Algorithm 873: LSTRS: MATLAB software for large-scale trust-region subproblems and regularization. (2008)
  12. Brezhneva, O. A.; Tret’yakov, A. A.: P-factor-approach to degenerate optimization problems (2006)
  13. Kearsley, Anthony J.: Matrix-free algorithm for the large-scale constrained trust-region subproblem (2006)
  14. Eldén, L.; Hansen, P. C.; Rojas, M.: Minimization of linear functionals defined on solutions of large-scale discrete ill-posed problems (2005)