Another benefit of using sparse matrices is that they can speed up some numerical linear algebra operations, such as solving linear systems, eigenvalue problems, or singular value decomposition.

Computational Linear Algebra for Coders This course is focused on the question: How do we do matrix computations with acceptable speed and acceptable accuracy? This course was taught in the University ...

Numerical linear algebra is the branch of mathematics that deals with solving linear systems, computing eigenvalues and eigenvectors, performing matrix decompositions, and applying numerical ...

William Layton has worked in numerical analysis for 40+ years, authoring around 200 papers and 5 books. He has advised more than 40 PhD students. He has taught numerical analysis and numerical linear ...

Direct methods for linear equations, matrix norms and condition numbers, orthogonal transformations, eigenvalues and eigenvectors, over determined systems, singular value decomposition, iterative ...

Sketching as a Tool for Numerical Linear Algebra highlights the recent advances in algorithms for numerical linear algebra that have come from the technique of linear sketching, whereby given a matrix ...

This simplifies the restarting mechanism and allows the introduction of several modifications. Convergence is assured by a monotonicity property that pushes the eigenvalues toward their limits. The ...

Abstract: Sketching as a Tool for Numerical Linear Algebra highlights the recent advances in algorithms for numerical linear algebra that have come from the technique of linear sketching, whereby ...

Direct methods for linear equations, matrix norms and condition numbers, orthogonal transformations, eigenvalues and eigenvectors, over determined systems, ... Numerical linear algebra (discontinued) ...