Volume 18, 2007Paris-sud working group on modelling and scientific computing 2006-2007
|Page(s)||70 - 86|
|Published online||12 September 2007|
Stochastic Matrices and Lp Norms : New Algorithms for Solving Ill-conditioned Linear Systems of Equations
Supelec, Plateau de Moulon, 3 rue Joliot-Curie, F-91192 Gif-sur-Yvette Cedex FRANCE
2 EDF R&D. 1, avenue du Général de Gaulle, F-92141 Clamart Cedex FRANCE
3 EDF R&D. 1, avenue du Général de Gaulle, F-92141 Clamart Cedex FRANCE
Corresponding authors: firstname.lastname@example.org email@example.com firstname.lastname@example.org email@example.com
We propose new iterative algorithms for solving a system of linear equations, possibly singular and inconsistent, presenting outstanding performances regarding ill-conditioning and error propagation. The basis of our approach is constructing with the l1 norm, a preconditioning matrix C (an approximation of a generalized inverse of the matrix) such that the preconditioned matrix CA is stochastic. This property allows us to retrieve, in an original way, the Schultz-Hotelling-Bodewig's algorithm of iterative refinement of the approximate inverse of a matrix. The approach, valid for non-negative matrices, is then generalized to any complex, rectangular matrix. We are then able to compute a generalized inverse of any matrix and this inverse is fit for use in classical solving schemes such as : Richardson-Tanabe, Schultz-Hotelling-Bodewig, preconditioned conjugate gradients and also in the Kaczmarz scheme (that we have generalized using lp norms). Regarding the obtained results on pathological well-known test-cases such as Hilbert and Nakasaka matrices, some of the proposed algorithms are empirically shown to be more efficient than the known classical techniques.
Key words: Linear system / stochastic matrix / norm / conditioning / Markov chain / generalized inverse.
© EDP Sciences, ESAIM, 2007
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