Pseudoinverse preconditioners and iterative methods for large dense linear least-squares problems
Journal: Bulletin of Computational Applied Mathematics (Bull CompAMa) (Vol.1, No. 1)Publication Date: 2013-06-30
Authors : Oskar Cahueñas; Luis M. Hernández-Ramos; Marcos Raydan;
Page : 25-47
Keywords : Schulz method; pseudoinverse; linear least-squares problems; preconditioned Richardson's method; conjugate gradient method;
Abstract
We address the issue of approximating the pseudoinverse of the coefficient matrix for dynamically building preconditioning strategies for the numerical solution of large dense linear least-squares problems. The new preconditioning strategies are embedded into simple and well-known iterative schemes that avoid the use of the, usually ill-conditioned, normal equations. We analyze a scheme to approximate the pseudoinverse, based on Schulz iterative method, and also different iterative schemes, based on extensions of Richardson's method, and the conjugate gradient method, that are suitable for preconditioning strategies. We present preliminary numerical results to illustrate the advantages of the proposed schemes.
Other Latest Articles
- Global improvements of a protein alignment algorithm and comparison with a global optimization solver
- The exponential distribution as the sum of discontinuous distributions
- Quantic Analysis of Formation of a Biomaterial of Latex, Retinol, and Chitosan for Biomedical Applications
- Artificial Neural Network Controller for Reducing the Total Harmonic Distortion (THD) in HVDC
- Surgical treatment with rhomboid excision and Limberg flap technique under spinal anesthesia of 23 young with pilonidal sinus disease
Last modified: 2018-08-05 10:48:55