Excerpt More than 25 years have passed since the first edition of this book was published in
1996. Least squares and least-norm problems have become more significant with every …

New restarted Lanczos bidiagonalization methods for the computation of a few of the largest
or smallest singular values of a large matrix are presented. Restarting is carried out by …

We derive a CUR approximate matrix factorization based on the discrete empirical
interpolation method (DEIM). For a given matrix \bfA, such a factorization provides a low …

A wide range of computational kernels in data mining and information retrieval from text
collections involve techniques from linear algebra. These kernels typically operate on data …

The increasing number of applications requiring the solution of large-scale singular value
problems has rekindled an interest in iterative methods for the SVD. Some promising recent …

The solution of time-dependent PDE-constrained optimization problems is a challenging
task in numerical analysis and applied mathematics. All-at-once discretizations and …

Tikhonov regularization is commonly used for the solution of linear discrete ill-posed
problems with error-contaminated data. A regularization parameter that determines the …

A Journey through the History of Numerical Linear Algebra: Back Matter Page 1 Bibliography
[1] A. Abdelfattah, H. Anzt, A. Bouteiller, A. Danalis, JJ Dongarra, M. Gates, A. Haidar, J. Kurzak …

Computing the singular values and vectors of a matrix is a crucial kernel in numerous
scientific and industrial applications. As such, numerous methods have been proposed to …

With nowadays steadily growing quantum processors, it is required to develop new quantum
tomography tools that are tailored for high-dimensional systems. In this work, we describe …