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Preconditioning techniques have emerged as an essential part of successful and efficient iterative solutions of matrices. Ke Chen's book offers a comprehensive introduction to these methods. A vast range of explicit and implicit sparse preconditioners are covered, including the conjugate gradient, multi-level and fast multi-pole methods, matrix and operator splitting, fast Fourier and wavelet transforms, incomplete LU and domain decomposition, Schur complements and approximate inverses. In addition, aspects of parallel realization using the MPI are discussed. Very much a users-guide, the book provides insight to the use of these techniques in areas such as acoustic wave scattering, image restoration and bifurcation problems in electrical power stations. Supporting MATLAB files are available from the Web to support and develop readers' understanding, and provide stimulus for further study. Pitched at graduate level, the book is intended to serve as a useful guide and reference for students, computational practitioners, engineers and researchers alike.
Matrices --- Differential equations --- Iterative methods (Mathematics) --- Integral equations --- Sparse matrices --- Equations différentielles --- Itération (Mathématiques) --- Equations intégrales --- Matrices éparses --- Numerical solutions --- Data processing --- Solutions numériques --- Informatique --- data processing --- Spare matrix techniques --- Equations, Integral --- Functional equations --- Functional analysis --- Iteration (Mathematics) --- Numerical analysis --- Data processing. --- 517.91 Differential equations --- Equations différentielles --- Itération (Mathématiques) --- Equations intégrales --- Matrices éparses --- Solutions numériques --- 517.91 --- Numerical solutions&delete& --- Sparse matrices - data processing --- Differential equations - Numerical solutions - Data processing --- Iterative methods (Mathematics) - Data processing --- Integral equations - Numerical solutions - Data processing
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Harry M Markowitz received the Nobel Prize in Economics in 1990 for his pioneering work in portfolio theory. He also received the von Neumann Prize from the Institute of Management Science and the Operations Research Institute of America in 1989 for his work in portfolio theory, sparse matrices and the SIMSCRIPT computer language. While Dr Markowitz is well-known for his work on portfolio theory, his work on sparse matrices remains an essential part of linear optimization calculations. In addition, he designed and developed SIMSCRIPT - a computer programming language. SIMSCRIPT has been widely
Investment analysis. --- Portfolio management. --- Sparse matrices. --- Analyse financière --- Gestion de portefeuille --- Matrices éparses --- Portfolio management --- -Investment analysis --- -Sparse matrices --- -330.9 --- Spare matrix techniques --- Matrices --- Analysis of investments --- Analysis of securities --- Security analysis --- Investment management --- Investment analysis --- Investments --- Securities --- Electronic information resources --- E-books --- AA / International- internationaal --- 305.91 --- 339.4 --- Econometrie van de financiële activa. Portfolio allocation en management. CAPM. Bubbles. --- Vermogensbeheer. Financiële analyse. Verspreiding van de beleggingsrisico's. --- Analyse financière --- Matrices éparses --- Sparse matrices --- Econometrie van de financiële activa. Portfolio allocation en management. CAPM. Bubbles --- Vermogensbeheer. Financiële analyse. Verspreiding van de beleggingsrisico's
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Here is an overview of modern computational stabilization methods for linear inversion, with applications to a variety of problems in audio processing, medical imaging, seismology, astronomy, and other areas. Rank-deficient problems involve matrices that are exactly or nearly rank deficient. Such problems often arise in connection with noise suppression and other problems where the goal is to suppress unwanted disturbances of given measurements. Discrete ill-posed problems arise in connection with the numerical treatment of inverse problems, where one typically wants to compute information about interior properties using exterior measurements. Examples of inverse problems are image restoration and tomography, where one needs to improve blurred images or reconstruct pictures from raw data. This book describes new and existing numerical methods for the analysis and solution of rank-deficient and discrete ill-posed problems. The emphasis is on insight into the stabilizing properties of the algorithms and the efficiency and reliability of the computations.
Equations, Simultaneous --- Iterative methods (Mathematics) --- Sparse matrices. --- Itération (Mathématiques) --- Matrices éparses --- Numerical solutions. --- 519.6 --- 681.3*G13 --- 517.95 --- #TELE:SISTA --- 681.3*G15 --- Computational mathematics. Numerical analysis. Computer programming --- Numerical linear algebra: conditioning; determinants; Eigenvalues; error analysis; linear systems; matrix inversion; pseudoinverses; sparse and very largesystems --- Partial differential equations --- Roots of nonlinear equations: convergence; error analysis; iterative methods;polynomials (Numerical analysis) --- 681.3*G15 Roots of nonlinear equations: convergence; error analysis; iterative methods;polynomials (Numerical analysis) --- 517.95 Partial differential equations --- 681.3*G13 Numerical linear algebra: conditioning; determinants; Eigenvalues; error analysis; linear systems; matrix inversion; pseudoinverses; sparse and very largesystems --- 519.6 Computational mathematics. Numerical analysis. Computer programming --- Iterative methods (Mathematics). --- Itération (Mathématiques) --- Matrices éparses --- Sparse matrices --- Spare matrix techniques --- Matrices --- Iteration (Mathematics) --- Numerical analysis --- Numerical solutions --- Equations, Simultaneous - Numerical solutions.
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