Linear Algebra: Algorithms, Applications, and Techniques - Tapa blanda

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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.

John T. Saccoman is Professor and Chair, Department of Mathematics and Computer Science, Seton Hall University, New Jersey received Ph.D., Stevens Institute of Technology, Hoboken, NJ, 1995 Research work on synthesis results in network reliability theory. He has published in several journals, authored supplementary materials, and is highly involved in the use of technology in applied mathematics. He has worked collaboratively on writings for Transforming the Curriculum Across the Disciplines Through Technology-Based Faculty Development and Writing-Intensive Course Redesign.

Daniel Gross is a professor in the Department of Mathematics and Computer Science at Seton Hall University in South Orange, New Jersey. Dan received his PhD in Mathematics from the University of Notre Dame in 1982. His research interests are network reliability and network vulnerability.

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Linear Algebra: Algorithms, Applications, and Techniques, Fourth Edition, begins with the concrete and computational, and guides the reader through the major applications. After an introductory chapter on properties of real numbers and proof techniques, the next three chapters address the basics: matrices, vector spaces, and linear transformations. The final four cover eigenvalues, applications, and Euclidean inner products. Appendices on Jordan canonical forms and Markov chains are included for further study.

This valuable resource offers a modern and algorithmic approach to computation while providing clear and straightforward theoretical background information. Throughout, this useful textbook presents broad and balanced views of theory, with key material highlighted in the text and summarized at the end of each chapter. To further support student practice, the book also includes ample exercises with answers and hints.