Linear Algebra with Mathematica: An Introduction Using Mathematica

by Fred Szabo

Overview

Linear Algebra: An Introduction With Mathematica uses a matrix-based presentation and covers the standard topics any mathematician will need to understand linear algebra while using Mathematica. Development of analytical and computational skills is emphasized, and worked examples provide step-by-step methods for solving basic problems using Mathematica. The subject's rich pertinence to problem solving across disciplines is illustrated with applications in engineering, the natural sciences, computer animation, and statistics.

Product Details

ISBN-13:	9780123814012
Publisher:	Elsevier Science
Publication date:	12/02/2009
Pages:	280
Product dimensions:	7.40(w) x 9.20(h) x 1.40(d)

About the Author

Author of:

The Linear Algebra Survival Guide, 1st Edition

Actuaries' Survival Guide, 2nd Edition

Actuaries' Survival Guide, 1st Edition

Linear Algebra: An Introduction using Maple, 1st Edition

Linear Algebra: An Introduction using Mathematica, 1st Edition

Fred E. Szabo is professor in the Department of Mathematics and Statistics at Concordia University in Canada. He completed his undergraduate studies at Oxford University under the guidance of Sir Michael Dummett and received a Ph.D. in mathematics from McGill University under the supervision of Joachim Lambek. After postdoctoral studies at Oxford University and visiting professorships at several European universities, he returned to Concordia University as a faculty member and dean of graduate studies. For more than twenty years, he developed methods for the teaching of mathematics with technology. In 2012 he was honored at the annual Wolfram Technology Conference for his work on "A New Kind of Learning" with a Wolfram Innovator Award. He is currently professor and Provost Fellow at Concordia University.

Table of Contents

Linear Systems.

Matrix Algebra.

Determinants.

Vector Spaces.

Linear Transformations.

Eigenvalues and Eigenvectors.

Norms and Inner Products.

Orthogonality.

Singular Values and Singular Vectors.