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.
Sprache
Verlagsort
Verlagsgruppe
Elsevier Science Publishing Co Inc
Zielgruppe
Für höhere Schule und Studium
Mathematics Undergraduates.
Maße
Höhe: 235 mm
Breite: 187 mm
Gewicht
ISBN-13
978-0-12-680135-4 (9780126801354)
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Schweitzer Klassifikation
Author of: The Linear Algebra Survival Guide, 1st EditionActuaries' Survival Guide, 2nd EditionActuaries' Survival Guide, 1st EditionLinear Algebra: An Introduction using Maple, 1st EditionLinear Algebra: An Introduction using Mathematica, 1st EditionFred 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.
Autor*in
Department of Mathematics, Concordia University, Montreal, Quebec, Canada
Linear Systems.
Matrix Algebra.
Determinants.
Vector Spaces.
Linear Transformations.
Eigenvalues and Eigenvectors.
Norms and Inner Products.
Orthogonality.
Singular Values and Singular Vectors.
