M
Michael T. Heath
Researcher at University of Illinois at Urbana–Champaign
Publications - 118
Citations - 10682
Michael T. Heath is an academic researcher from University of Illinois at Urbana–Champaign. The author has contributed to research in topics: Parallel algorithm & Sparse matrix. The author has an hindex of 38, co-authored 116 publications receiving 10191 citations. Previous affiliations of Michael T. Heath include National Center for Supercomputing Applications & Duke University.
Papers
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Journal ArticleDOI
Generalized Cross-Validation as a Method for Choosing a Good Ridge Parameter
TL;DR: The generalized cross-validation (GCV) method as discussed by the authors is a generalized version of Allen's PRESS, which can be used in subset selection and singular value truncation, and even to choose from among mixtures of these methods.
Book
Scientific Computing: An Introductory Survey
Michael T. Heath,Eric M. Munson +1 more
TL;DR: This book presents a broad overview of numerical methods for solving all the major problems in scientific computing, including linear and nonlinear equations, least squares, eigenvalues, optimization, interpolation, integration, ordinary and partial differential equations, fast Fourier transforms, and random number generators.
Journal ArticleDOI
Computation of system balancing transformations and other applications of simultaneous diagonalization algorithms
TL;DR: It is shown that a similar approach may be taken, involving the generalized singular value decomposition of a certain product of matrices without explicitly forming the product, to the classical simultaneous diagonalization problem.
Book
Visualizing the performance of parallel programs
TL;DR: ParaGraph as mentioned in this paper is a software tool that provides a detailed, dynamic, graphical animation of the behavior of message-passing parallel programs and graphical summaries of their performance, animating trace information from actual runs to depict behavior and obtain the performance summaries.
Journal ArticleDOI
Visualizing the performance of parallel programs
TL;DR: ParaGraph animates trace information from actual runs to depict behavior and obtain the performance summaries, and provides twenty-five perspectives on the same data, lending insight that might otherwise be missed.