Data-driven discovery is revolutionizing how we model, predict, and control complex
systems. Now with Python and MATLAB®, this textbook trains mathematical scientists and …

Matrix computations are both fundamental and ubiquitous in computational science and its
vast application areas. Along with the development of more advanced computer systems …

Publisher Summary This paper describes the recent advances for rapidly and accurately
solving matrix Riccati and Sylvester equations and applies them to devise efficient …

Controllability and observability have long been recognized as fundamental structural
properties of dynamical systems, but have recently seen renewed interest in the context of …

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 …

In the nearly seven years since I finished writing the first edition of this book research on the
accuracy and stability of numerical algorithms has continued to flourish and mature. Our …

In today's technological world, physical and artificial processes are mainly described by
mathematical models, which can be used for simulation or control. These processes are …

In addition to providing high-profile successes in computer vision and natural language
processing, neural networks also provide an emerging set of techniques for scientific …

The problem of approximating a multivariable transfer function G (s) of McMillan degree n,
by Ĝ (s) of McMillan degree k is considered. A complete characterization of all …

The standard theory of decision making under uncertainty advises the decision maker to
form a statistical model linking outcomes to decisions and then to choose the optimal …