Numerical simulation of large-scale dynamical systems plays a fundamental role in studying
a wide range of complex physical phenomena; however, the inherent large-scale nature of …

The computational solution of problems can be restricted by the availability of solution
methods for linear (ized) systems of equations. In conjunction with iterative methods …

Global machine learning force fields, with the capacity to capture collective interactions in
molecular systems, now scale up to a few dozen atoms due to considerable growth of model …

Spectral Graph Neural Networks (GNNs), alternatively known as graph filters, have gained
increasing prevalence for heterophily graphs. Optimal graph filters rely on Laplacian …

Shortcuts to adiabaticity provide fast protocols for quantum state preparation in which the
use of auxiliary counterdiabatic controls circumvents the requirement of slow driving in …

Memristor-enabled in-memory computing provides an unconventional computing paradigm
to surpass the energy efficiency of von Neumann computers. Owing to the limitation of the …

Given the square matrices A,B,D,E and the matrix C of conforming dimensions, we consider
the linear matrix equation A\mathbfXE+D\mathbfXB=C in the unknown matrix \mathbfX. Our …

We develop a novel, fundamental, and surprisingly simple randomized iterative method for
solving consistent linear systems. Our method has six different but equivalent interpretations …

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 …

This book presents a broad overview of numerical methods for students and professionals in
computationally oriented disciplines who need to solve mathematical problems. It differs …