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In this book the authors present classical and recent results for matrix analysis that have proved to be important to applied mathematics. Facts about matrices, beyond those found in an elementary linear algebra course, are needed to understand virtually any area of mathematics, and the necessary material has only occurred sporadically in the literature and university curricula. As the interest in applied mathematics has grown, the need for a text and a reference work offering a broad selection of topics has become apparent, and this book aims to meet that need. This book will be welcomed as an undergraduate or graduate textbook for students studying matrix analysis. The authors assume a background in elementary linear algebra and knowledge of rudimentary analytical concepts. They begin with a review and discussion of eigenvalues and eigenvectors. The following chapters each treat a major topic in depth. This volume should be useful not only as a text, but also as a self-contained reference work to a variety of audiences in other scientific fields.
'There seems little doubt that the book will become a standard reference for research workers in numerical mathematics.' Computing Reviews
'The reviewer strongly recommends that those working in either pure or applied linear algebra have this book on their desks.' SIAM Review
'This will doubtless be the standard text for years to come.' American Scientists
'On the whole the authors have done an excellent job of supplying linear algebraists and applied mathematicians with a well-organized comprehensive survey, which can serve both as a text and as a reference. The reviewer recommends that everyone working in these fields have this book on his/her desk.' Linear Algebra and its Applications
Matrix Analysis presents the classical and recent results for matrix analysis that have proved to be important to applied mathematics. Facts about matrices, beyond those found in elementary linear algebra courses, are needed to understand most areas of mathematics. This book offers a broad selection of topics for students of matrix analysis.
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