In Demmel et al. (Numer. Math. 106(2), 199-224, 2007) we showed that a large class of fast recursive matrix multiplication algorithms is stable in a normwise sense, and that in fact if multiplication of n-by-n matrices can be done by any algorithm in O(n(omega+eta)) operations for any eta > 0, then it can be done stably in O(n(omega+eta)) operations for any eta > 0. Here we extend this result to show that essentially all standard linear algebra operations, including LU decomposition, QR decomposition, linear equation solving, matrix inversion, solving least squares problems, (generalized) eigenvalue problems and the singular value decomposition can also be done stably (in a normwise sense) in O(n(omega+eta)) operations
AbstractWe give a general method for showing that all numberings of certain effective algebras are r...
For an $n \times n$ tridiagonal matrix we exploit the structure of its QR factorization to devis...
AbstractThe numbers of bit operations (bt) required for matrix multiplication (MM), matrix inversion...
We perform forward error analysis for a large class of recursive matrix multiplication algorithms in...
We perform forward error analysis for a large class of recursive matrix multiplication algorithms in...
Abstract. We survey the numerical stability of some fast algorithms for solving systems of linear eq...
We survey the numerical stability of some fast algorithms for solving systems of linear equations an...
Fast algorithms for matrix multiplication, namely those that perform asymptotically fewer scalar ope...
The complexity of matrix multiplication (hereafter MM) has been intensively studied since 1969, when...
AbstractWe consider the problem of finding a basic solution to a system of linear constraints (in st...
International audienceThis paper introduces hybrid LU-QR algorithms for solving dense linear sys-tem...
AbstractThe stability of algorithms in numerical linear algebra is discussed. The concept of stabili...
Numerical algorithms are considered for three distinct areas of numerical linear algebra: hyperbolic...
AbstractWe present several bilinear algorithms for the acceleration of multiplication of n X n matri...
n the last two decades several NC algorithms for solving basic linear algebraic problems have appear...
AbstractWe give a general method for showing that all numberings of certain effective algebras are r...
For an $n \times n$ tridiagonal matrix we exploit the structure of its QR factorization to devis...
AbstractThe numbers of bit operations (bt) required for matrix multiplication (MM), matrix inversion...
We perform forward error analysis for a large class of recursive matrix multiplication algorithms in...
We perform forward error analysis for a large class of recursive matrix multiplication algorithms in...
Abstract. We survey the numerical stability of some fast algorithms for solving systems of linear eq...
We survey the numerical stability of some fast algorithms for solving systems of linear equations an...
Fast algorithms for matrix multiplication, namely those that perform asymptotically fewer scalar ope...
The complexity of matrix multiplication (hereafter MM) has been intensively studied since 1969, when...
AbstractWe consider the problem of finding a basic solution to a system of linear constraints (in st...
International audienceThis paper introduces hybrid LU-QR algorithms for solving dense linear sys-tem...
AbstractThe stability of algorithms in numerical linear algebra is discussed. The concept of stabili...
Numerical algorithms are considered for three distinct areas of numerical linear algebra: hyperbolic...
AbstractWe present several bilinear algorithms for the acceleration of multiplication of n X n matri...
n the last two decades several NC algorithms for solving basic linear algebraic problems have appear...
AbstractWe give a general method for showing that all numberings of certain effective algebras are r...
For an $n \times n$ tridiagonal matrix we exploit the structure of its QR factorization to devis...
AbstractThe numbers of bit operations (bt) required for matrix multiplication (MM), matrix inversion...