Scientific Computing: An Introductory SurveyMcGraw-Hill, 2002 - 563 pagine Scientific Computing, 2/e, presents a broad overview of numerical methods for solving all the major problems in scientific computing, including linear and nonlinearequations, 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. |
Dall'interno del libro
Risultati 1-3 di 84
Pagina 301
... Determine the critical points of each of the following functions and characterize each as a min- imum , maximum , or saddle point . Also determine whether each function has a global minimum or maximum on R2 . ( a ) f ( x , y ) = x2 ...
... Determine the critical points of each of the following functions and characterize each as a min- imum , maximum , or saddle point . Also determine whether each function has a global minimum or maximum on R2 . ( a ) f ( x , y ) = x2 ...
Pagina 306
... determine V and Km . for the remaining methods . You may need to use. In a typical experiment , vo is measured as S is varied , and then V and Km are to be determined from the resulting data . ( a ) Given the measured data , t 0.00 0.25 ...
... determine V and Km . for the remaining methods . You may need to use. In a typical experiment , vo is measured as S is varied , and then V and Km are to be determined from the resulting data . ( a ) Given the measured data , t 0.00 0.25 ...
Pagina 443
... determine the so- lution to the BVP for ƒ ( t ) = 1 . ( f ) Use the Green's function to determine the so- lution to the BVP for f ( t ) = t . ( g ) Use the Green's function to determine the so- lution to the BVP for ƒ ( t ) = t2 . 10.2 ...
... determine the so- lution to the BVP for ƒ ( t ) = 1 . ( f ) Use the Green's function to determine the so- lution to the BVP for f ( t ) = t . ( g ) Use the Green's function to determine the so- lution to the BVP for ƒ ( t ) = t2 . 10.2 ...
Sommario
Scientific Computing | 1 |
Initial Value Problems for ODEs | 9 |
Systems of Linear Equations | 49 |
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Scientific Computing: An Introductory Survey, Revised Second Edition Michael T. Heath Anteprima limitata - 2018 |
Scientific Computing: An Introductory Survey, Revised Second Edition Michael T. Heath Anteprima limitata - 2018 |
Parole e frasi comuni
accuracy algorithm approximate solution basis functions Cholesky factorization coefficients column complex components compute condition number constraints convergence rate corresponding data points defined derivative determine differential digits dimension eigenvalues eigenvectors Euler's method evaluate Example finite difference floating-point number floating-point system function f function values Gaussian elimination given golden section search gradient hence Hessian matrix Householder transformation implementation initial input integral integrand interval inverse inverse iteration iterative method Lagrange least squares problem library routine linear equations linear least squares linear system LU factorization MATLAB method for solving minimizing multiple n x n Newton's method nonlinear equations nonsingular nonzero norm objective function obtain optimization problem orthogonal parameters perturbations pivoting polynomial interpolation positive definite programming QR factorization QR iteration quadratic quadrature rule relative residual resulting root secant method Section sequence singular value spline symmetric system Ax tion tridiagonal True or false upper triangular vector zero