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The second edition of the author’s acclaimed textbook covers the major topics of computational linear algebra, including solution of a system of linear equations, least-squares solutions of linear systems, and computation of eigenvalues, eigenvectors, and singular value problems. The important features of the original edition have been updated and improved in the second edition: The author covers a variety of motivating applications drawn from numerous disciplines of science and engineering. When a physical problem is posed, the scientific and engineering significance of the solution is clearly stated. Each chapter contains a summary of the important concepts developed in that chapter, suggestions for further reading, and numerous exercises, both theoretical and MATLAB and MATCOM based. The author also provides a list of key words for quick reference. The MATLAB toolkit MATCOM contains implementations of the major algorithms associated with the book and enables students to study different algorithms for the same problem, comparing efficiency, stability, and accuracy. The topics of generalized and quadratic eigenvalue problems, which arise in practical engineering applications, are described in great detail. This feature, along with an important overview of Krylov subspace methods and an extensively updated bibliography, enhances the book’s value as a reference for both engineers and students. This book is intended for undergraduate and postgraduate students in applied and computational mathematics, scientific computing, computer science, financial mathematics, actuarial sciences, and electrical and mechanical engineering. About the Author BISWA NATH DATTA is Professor of Mathematical Sciences, Adjunct Professor of Electrical and Mechanical Engineering, and Distinguished Research Professor at Northern Illinois University. Table of Contents Preface 1. Linear Algebra Problems, Their Importance, and Computational Difficulties 2.  A Review of Some Required Concepts from Core Linear Algebra 3. Floating Point Numbers and Errors in Computations 4. Stability of Algorithms and Conditioning of Problems 5. Gaussian Elimination and LU Factorization 6. Numerical Solutions of Linear Systems 7. QR Factorization, Singular Value Decomposition, and Projections 8. Least-Squares Solutions to Linear Systems 9. Numerical Matrix Eigenvalue Problems 10. Numerical Symmetric Eigenvalue Problem and Singular Value Decomposition 11. Generalized and Quadratic Egenvalue Problems 12. Iterative Methods for Large and Sparse Problems: An Overview 13. Some Key Terms in Numerical Linear Algebra Bibliography Index