現代科学計算における数値解析（第２版）<br>Numerical Analysis in Modern Scientific Computing : An Introduction (Texts in Applied Mathematics) 〈Vol.43〉 （2ND）

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現代科学計算における数値解析（第２版）
Numerical Analysis in Modern Scientific Computing : An Introduction (Texts in Applied Mathematics) 〈Vol.43〉 （2ND）

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  • 製本 Hardcover:ハードカバー版／ページ数 360 p.
  • 商品コード 9780387954103

Full Description

Mathematics is playing an ever more important role in the physical and biological sciences, provoking a blurring of boundaries between scientific disciplines and a resurgence of interest in the modern as well as the clas­ sical techniques of applied mathematics. This renewal of interest, both in research and teaching, has led to the establishment of the series Texts in Applied Mathematics (TAM). The development of new courses is a natural consequence of a high level of excitement on the research frontier as newer techniques, such as numerical and symbolic computer systems, dynamical systems, and chaos, mix with and reinforce the traditional methods of applied mathematics. Thus, the purpose of this textbook series is to meet the current and future needs of these advances and to encourage the teaching of new courses. TAM will publish textbooks suitable for use in advanced undergraduate and beginning graduate courses, and will complement the Applied Mathe­ matical Sciences (AMS) series, which will focus on advanced textbooks and research-level monographs.

Contents

1 Linear Systems.- 1.1 Solution of Triangular Systems.- 1.2 Gaussian Elimination.- 1.3 Pivoting Strategies and Iterative Refinement.- 1.4 Cholesky Decomposition for Symmetric Positive Definite Matrices.- Exercises.- 2 Error Analysis.- 2.1 Sources of Errors.- 2.2 Condition of Problems.- 2.3 Stability of Algorithms.- 2.4 Application to Linear Systems.- Exercises.- 3 Linear Least-Squares Problems.- 3.1 Least-Squares Method of Gauss.- 3.2 Orthogonalization Methods.- 3.3 Generalized Inverses.- Exercises.- 4 Nonlinear Systems and Least-Squares Problems.- 4.1 Fixed-Point Iterations.- 4.2 Newton Methods for Nonlinear Systems.- 4.3 Gauss-Newton Method for Nonlinear Least-Squares Problems.- 4.4 Nonlinear Systems Depending on Parameters.- Exercises.- 5 Linear Eigenvalue Problems.- 5.1 Condition of General Eigenvalue Problems.- 5.2 Power Method.- 5.3 QR-Algorithm for Symmetric Eigenvalue Problems.- 5.4 Singular Value Decomposition.- 5.5 Stochastic Eigenvalue Problems.- Exercises.- 6 Three-Term Recurrence Relations.- 6.1 Theoretical Background.- 6.2 Numerical Aspects.- 6.3 Adjoint Summation.- Exercises.- 7 Interpolation and Approximation.- 7.1 Classical Polynomial Interpolation.- 7.2 Trigonometric Interpolation.- 7.3 Bézier Techniques.- 7.4 Splines.- Exercises.- 8 Large Symmetric Systems of Equations and Eigenvalue Problems.- 8.1 Classical Iteration Methods.- 8.2 Chebyshev Acceleration.- 8.3 Method of Conjugate Gradients.- 8.4 Preconditioning.- 8.5 Lanczos Methods.- Exercises.- 9 Definite Integrals.- 9.1 Quadrature Formulas.- 9.2 Newton-Cotes Formulas.- 9.3 Gauss-Christoffel Quadrature.- 9.4 Classical Romberg Quadrature.- 9.5 Adaptive Romberg Quadrature.- 9.6 Hard Integration Problems.- 9.7 Adaptive Multigrid Quadrature.- Exercises.- References.- Software.