numerical methods large eigenvalue de saad (12 resultados)

PAP. Condición: New. New Book. Shipped from UK. Established seller since 2000.

Paperback. Condición: New. This revised edition discusses numerical methods for computing the eigenvalues and eigenvectors of large sparse matrices. It provides an in-depth view of the numerical methods that are applicable for solving matrix eigenvalue problems that arise in various engineering and scientific applications. Each chapter was updated by shortening or deleting outdated topics, adding topics of more recent interest and adapting the Notes and References section. Significant changes have been made to Chapters 6 through 8, which describe algorithms and their implementations and now include topics such as the implicit restart techniques, the Jacobi-Davidson method and automatic multilevel substructuring.

Condición: New. 2011. Revised. paperback. . . . . .

Paperback. Condición: Brand New. revised edition. 276 pages. 8.75x6.00x0.75 inches. In Stock.

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Condición: New. 2011. Revised. paperback. . . . . . Books ship from the US and Ireland.

First Edition. Near-fine copy in the original illustrated, paper-covered boards. Spine bands and panel edges slightly dulled and dust-toned as with age. Corners sharp with an overall tight, bright and clean impression. Physical description; 346 pages : illustrations ; 24 cm. Notes: Includes bibliographical references (pages 323-340) and index.Contents: I. Background in Matrix Theory and Linear Algebra. 1. Matrices. 2. Square Matrices and Eigenvalues. 3. Types of Matrices. 4. Vector Inner Products and Norms. 5. Matrix Norms. 6. Subspaces. 7. Orthogonal Vectors and Subspaces. 8. Canonical Forms of Matrices. 9. Normal and Hermitian Matrices. 10. Nonnegative Matrices -- II. Sparse Matrices. 1. Introduction. 2. Storage Schemes. 3. Basic Sparse Matrix Operations. 4. Sparse Direct Solution Methods. 5. Test Problems. 6. SPARSKIT -- III. Perturbation Theory and Error Analysis. 1. Projectors and their Properties. 2. A-Posteriori Error Bounds. 3. Conditioning of Eigen-problems. 4. Localization Theorems -- IV. The Tools of Spectral Approximation. 1. Single Vector Iterations. 2. Deflation Techniques. 3. General Projection Methods. 4. Chebyshev Polynomials -- V. Subspace Iteration. 1. Simple Subspace Iteration. 2. Subspace Iteration with Projection. 3. Practical Implementations -- VI. Krylov Subspace Methods. 1. Krylov Subspaces. 2. Arnoldi's Method.3. The Hermitian Lanczos Algorithm. 4. Non-Hermitian Lanczos Algorithm. 5. Block Krylov Methods. 6. Convergence of the Lanczos Process. 7. Convergence of the Arnoldi Process -- VII. Acceleration Techniques and Hybrid Methods. 1. The Basic Chebyshev Iteration. 2. Arnoldi-Chebyshev Iteration. 3. Deflated Arnoldi-Chebyshev. 4. Chebyshev Subspace Iteration. 5. Least Squares -- Arnoldi -- VIII. Preconditioning Techniques. 1. Shift-and-invert Preconditioning. 2. Polynomial Preconditioning. 3. Davidson's Method. 4. Generalized Arnoldi Algorithms -- IX. Non-Standard Eigenvalue Problems. 1. Introduction. 2. Generalized Eigenvalue Problems. 3. Quadratic Problems -- X. Origins of Matrix Eigenvalue Problems. 1. Introduction. 2. Mechanical Vibrations. 3. Electrical Networks. 4. Quantum Chemistry. 5. Stability of Dynamical Systems. 6. Bifurcation Analysis. 7. Chemical Reactions. 8. Macro-economics. 9. Markov Chain Models. Subjects: Nonsymmetric matrices.Eigenvalues. Matrices asymétriques. Valeurs propres. Eigenvalues.Nonsymmetric matrices.Valeurs propres. Matrices.Matrices 3 Kg.

First Edition. Near-fine copy in the original illustrated, paper-covered boards. Spine bands and panel edges slightly dulled and dust-toned as with age. Corners sharp with an overall tight, bright and clean impression. Physical description; 346 pages : illustrations ; 24 cm. Notes: Includes bibliographical references (pages 323-340) and index.Contents: I. Background in Matrix Theory and Linear Algebra. 1. Matrices. 2. Square Matrices and Eigenvalues. 3. Types of Matrices. 4. Vector Inner Products and Norms. 5. Matrix Norms. 6. Subspaces. 7. Orthogonal Vectors and Subspaces. 8. Canonical Forms of Matrices. 9. Normal and Hermitian Matrices. 10. Nonnegative Matrices -- II. Sparse Matrices. 1. Introduction. 2. Storage Schemes. 3. Basic Sparse Matrix Operations. 4. Sparse Direct Solution Methods. 5. Test Problems. 6. SPARSKIT -- III. Perturbation Theory and Error Analysis. 1. Projectors and their Properties. 2. A-Posteriori Error Bounds. 3. Conditioning of Eigen-problems. 4. Localization Theorems -- IV. The Tools of Spectral Approximation. 1. Single Vector Iterations. 2. Deflation Techniques. 3. General Projection Methods. 4. Chebyshev Polynomials -- V. Subspace Iteration. 1. Simple Subspace Iteration. 2. Subspace Iteration with Projection. 3. Practical Implementations -- VI. Krylov Subspace Methods. 1. Krylov Subspaces. 2. Arnoldi's Method.3. The Hermitian Lanczos Algorithm. 4. Non-Hermitian Lanczos Algorithm. 5. Block Krylov Methods. 6. Convergence of the Lanczos Process. 7. Convergence of the Arnoldi Process -- VII. Acceleration Techniques and Hybrid Methods. 1. The Basic Chebyshev Iteration. 2. Arnoldi-Chebyshev Iteration. 3. Deflated Arnoldi-Chebyshev. 4. Chebyshev Subspace Iteration. 5. Least Squares -- Arnoldi -- VIII. Preconditioning Techniques. 1. Shift-and-invert Preconditioning. 2. Polynomial Preconditioning. 3. Davidson's Method. 4. Generalized Arnoldi Algorithms -- IX. Non-Standard Eigenvalue Problems. 1. Introduction. 2. Generalized Eigenvalue Problems. 3. Quadratic Problems -- X. Origins of Matrix Eigenvalue Problems. 1. Introduction. 2. Mechanical Vibrations. 3. Electrical Networks. 4. Quantum Chemistry. 5. Stability of Dynamical Systems. 6. Bifurcation Analysis. 7. Chemical Reactions. 8. Macro-economics. 9. Markov Chain Models. Subjects: Nonsymmetric matrices.Eigenvalues. Matrices asymétriques. Valeurs propres. Eigenvalues.Nonsymmetric matrices.Valeurs propres. Matrices.Matrices 1 Kg.

Paperback. Condición: New. This revised edition discusses numerical methods for computing the eigenvalues and eigenvectors of large sparse matrices. It provides an in-depth view of the numerical methods that are applicable for solving matrix eigenvalue problems that arise in various engineering and scientific applications. Each chapter was updated by shortening or deleting outdated topics, adding topics of more recent interest and adapting the Notes and References section. Significant changes have been made to Chapters 6 through 8, which describe algorithms and their implementations and now include topics such as the implicit restart techniques, the Jacobi-Davidson method and automatic multilevel substructuring.

First Edition. Fine copy in the original paper-covered boards. Slightest suggestion only of dust-dulling to the spine bands and panel edges. Remains particularly well-preserved overall; tight, bright, clean and strong. Physical description: 346 pages : illustrations ; 24 cm. Subject: Nonsymmetric matrices. Eigenvalues. Matrices. 3 Kg.

First Edition. Fine copy in the original paper-covered boards. Slightest suggestion only of dust-dulling to the spine bands and panel edges. Remains particularly well-preserved overall; tight, bright, clean and strong. Physical description: 346 pages : illustrations ; 24 cm. Subject: Nonsymmetric matrices. Eigenvalues. Matrices. 1 Kg.

Softcover. Ex-library with stamp and library-signature. GOOD condition, some traces of use. Ancien Exemplaire de bibliothèque avec signature et cachet. BON état, quelques traces d'usure. Ehem. Bibliotheksexemplar mit Signatur und Stempel. GUTER Zustand, ein paar Gebrauchsspuren. 65 SAA 9780719033865 Sprache: Englisch Gewicht in Gramm: 450.