Matrix Methods: Applied Linear Algebra, Third Edition, as a textbook, provides a unique and comprehensive balance between the theory and computation of matrices. The application of matrices is not just for mathematicians. The use by other disciplines has grown dramatically over the years in response to the rapid changes in technology. Matrix methods is the essence of linear algebra and is what is used to help physical scientists; chemists, physicists, engineers, statisticians, and economists solve real world problems.
Auflage
Sprache
Verlagsort
Verlagsgruppe
Elsevier Science Publishing Co Inc
Zielgruppe
Für höhere Schule und Studium
Upper-level undergraduate and graduate students in applied mathematics, computer science, engineering and science
Maße
Höhe: 235 mm
Breite: 191 mm
Gewicht
ISBN-13
978-0-12-374427-2 (9780123744272)
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Schweitzer Klassifikation
Richard Bronson is a Professor of Mathematics and Computer Science at Fairleigh Dickinson University and is Senior Executive Assistant to the President. Ph.D., in Mathematics from Stevens Institute of Technology. He has written several books and numerous articles on Mathematics. He has served as Interim Provost of the Metropolitan Campus, and has been Acting Dean of the College of Science and Engineering at the university in New Jersey Gabriel B. Costa is currently a visiting professor at the United States Military Academy at West Point and is on the faculty at Seton Hall. And is an engineer. He holds many titles and fills them with distinction. He has a B.S., M.S. and Ph.D. in Mathematics from Stevens Institute of Technology. He has also co-authored another Academic Press book with Richard Bronson, Matrix Methods.
Autor*in
Professor of Mathematics and Computer Science, Senior Executive Assistant to the President, Fairleigh Dickinson University, USA
Visiting Professor, Department of Mathematical Sciences, United States Military Academy, West Point, NY, USA
Chapter 1: Matrices
Chapter 2: Simultaneous Linear Equations
Chapter 3: The Inverse
Chapter 4: Linear Programming
Chapter 5: Determinants
Chapter 6: Eigenvalues and Eigenvectors
Chapter 7: Matrix Calculus
Chapter 8: Linear Differential Equations
Chapter 9: Markov Chains
Chapter 10: Real Inner Products and Least Squares
Appendix: Computational Tools and technology
