Matrix computations (3rd ed.)
Iterative methods for solving linear systems
Iterative methods for solving linear systems
Fast Probabilistic Algorithms for Verification of Polynomial Identities
Journal of the ACM (JACM)
Structured matrices and polynomials: unified superfast algorithms
Structured matrices and polynomials: unified superfast algorithms
Numerical Linear Algebra for High Performance Computers
Numerical Linear Algebra for High Performance Computers
Preconditioning techniques for large linear systems: a survey
Journal of Computational Physics
Probabilistic algorithms for sparse polynomials
EUROSAM '79 Proceedings of the International Symposiumon on Symbolic and Algebraic Computation
The condition number of a randomly perturbed matrix
Proceedings of the thirty-ninth annual ACM symposium on Theory of computing
Effect of small rank modification on the condition number of a matrix
Computers & Mathematics with Applications
Additive preconditioning and aggregation in matrix computations
Computers & Mathematics with Applications
Schur aggregation for linear systems and determinants
Theoretical Computer Science
Real and complex polynomial root-finding with eigen-solving and preprocessing
Proceedings of the 2010 International Symposium on Symbolic and Algebraic Computation
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Our weakly random additive preconditioners facilitate the solution of linear systems of equations and other fundamental matrix computations. Compared to the popular SVD-based multiplicative preconditioners, these preconditioners are generated more readily and for a much wider class of input matrices. Furthermore they better preserve matrix structure and sparseness and have a wider range of applications, in particular to linear systems with rectangular coefficient matrices. We study the generation of such preconditioners and their impact on conditioning of the input matrix. Our analysis and experiments show the power of our approach even where we use very weak randomization and choose sparse and/or structured preconditioners.