## Scientific computing: an introductory surveyScientific Computing, 2/e, presents a broad overview of numerical methods for solving all the major problems in scientific computing, including linear and nonlinear equations, least squares, eigenvalues, optimization, interpolation, integration, ordinary and partial differential equations, fast Fourier transforms, and random number generators. The treatment is comprehensive yet concise, software-oriented yet compatible with a variety of software packages and programming languages. The book features more than 160 examples, 500 review questions, 240 exercises, and 200 computer problems. Changes for the second edition include: expanded motivational discussions and examples; formal statements of all major algorithms; expanded discussions of existence, uniqueness, and conditioning for each type of problem so that students can recognize "good" and "bad" problem formulations and understand the corresponding quality of results produced; and expanded coverage of several topics, particularly eigenvalues and constrained optimization. The book contains a wealth of material and can be used in a variety of one- or two-term courses in computer science, mathematics, or engineering. Its comprehensiveness and modern perspective, as well as the software pointers provided, also make it a highly useful reference for practicing professionals who need to solve computational problems. |

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I have read the third chapter of least square method, which is quite helpful. And this book is a recommended book for our subject "computational method". Hence I think it is a very good introductory level book, which also gives literature review for further reading.

#### Review: Scientific Computing

User Review - Tieta - GoodreadsGreat book about numerical analysis. Favorite quote: "We cannot live without approximations"! :) Read full review

### Contents

Scientific Computing | 1 |

Systems of Linear Equations | 49 |

Linear Least Squares | 105 |

Copyright | |

12 other sections not shown

### Common terms and phrases

accuracy algebraic algorithm approximate solution basis functions boundary conditions Cholesky factorization coefficients column complex components condition number conjugate gradient method convergence rate corresponding data points derivative determine diagonal entries differential equations digits dimension eigenvalues eigenvectors Euler's method evaluate example finite difference floating-point number floating-point system Gaussian elimination given hence Hessian matrix Householder transformation implementation input integral integrand interval inverse inverse iteration iterative method least squares problem library routine linear equations linear least squares linear system LU factorization MATLAB mesh method for solving minimum multiple Newton's method nodes nonlinear equations nonsingular nonzero norm obtain optimization orthogonal parameters PDEs perturbations pivoting plot polynomial interpolation positive definite QR factorization QR iteration quadratic quadrature rule random numbers relative residual resulting root scalar secant method Section sequence singular value spline stability step subinterval symmetric tion tridiagonal True or false upper triangular vector zero