Solving systems of algebraic linear equations is among the most frequent problems in
scientific computing. It appears in many areas like physics, engineering, chemistry, biology …

Global Krylov subspace methods are effective iterative solvers for large linear matrix
equations. Several Lanczos-type product methods (LTPMs) for solving standard linear …

We introduce M stab, a Krylov subspace recycling method for the iterative solution of
sequences of linear systems, where the system matrix is fixed and is large, sparse, and …

Solving sparse linear systems is a critical challenge in many scientific and engineering
fields, particularly when these systems are severely ill-conditioned. This work aims to …

[16] P.-A. Absil, R. Mahony, and B. Andrews. Convergence of the iterates of descent
methods for analytic cost functions. SIAM J. Optim., 16 (2): 531–547, 2005.[17] A. Abu-Omar …

Global Krylov subspace methods are effective iterative solvers for large linear matrix
equations. Several Lanczos-type product methods (LTPMs) for solving standard linear …

In this thesis we propose a novel implementation of IDRstab that avoids several unlucky
breakdowns of current IDRstab implementations and is further capable of benefiting from a …

As the basic research applied in pattern recognition, machine leaning, data mining and
other fields, the main purpose of feature extraction is to achieve low loss of data …

We propose Mstab, a novel Krylov subspace recycling method for the iterative solution of
sequences of linear systems with fixed system matrix and changing right-hand sides. This …

Krylov subspace methods are a common way to approximate large linear systems. The aim
of this report is to build a new version of the Krylov algorithm IDRstab in MATLAB and test its …