R
robin
| I think the
| Numerical Recipes books are excellent introductions to many
| computational problems, including linear algebra. I do agree that
| the code itself that is given in the book is sometimes not the most
| robust, but on the other hand, something like LAPACK code is robust
| but too complicated for a beginner to understand easily.
Numerical Recipes is an authoritative source of information
about a vast number of numerical procedures.
Therefore I endorse Ron Shepherd's remark that it is an
"excellent introductions to many computational problems, including linear algebra".
At more than 1200 pages, and more than 400 propgrams/procedures,
there is something there for everybody.
Three editions have been published.
________________
New in the Third Edition (2007):
* A new chapter on classification and inference,
including such topics as Gaussian mixture models,
hidden Markov modeling, hierarchical clustering
(phylogenetic trees), and support vector machines.
* A new chapter on computational geometry, including
topics like KD trees, quad- and octrees, Delaunay
triangulation and applications, and many useful
algorithms for lines, polygons, triangles, spheres, and
so on.
* Many new statistical distributions, with pdf's, cdf's,
and inverse cdf's
* An expanded treatment of ODEs, emphasizing recent
advances, and with completely new routines
* Much-expanded sections on uniform random
deviates. and on deviates from many other statistical
distributions
* An introduction to spectral and pseudospectral
methods for PDEs
* Interior point methods for linear programming
* More on sparse matrices
* Interpolation on scattered data in multidimensions
* Curve interpolation in multidimensions
* Quadrature by variable transformation, and adaptive
quadrature
* More on Gaussian quadratures and orthogonal
polynomials
* More on accelerating the convergence of series
* Improved incomplete gamma and beta functions,
and new inverse functions
* Improved spherical harmonics and fast spherical
harmonic transforms
* Generalized Fermi-Dirac integrals
* Multivariate Gaussian deviates
* Algorithms and implementations for hash memory
functions
* Incremental quantile estimation
* Chi-square with small numbers of counts
* Dynamic programming
* Hard and soft error correction, and Viterbi decoding
* Eigensystem routines for real, nonsymmetric matrices
* Multitaper methods for power spectral estimation
* Wavelets on the interval
* Information-theoretic properties of distributions
* Markov chain Monte Carlo
* Gaussian process regression and Kriging
* Stochastic simulation of chemical reaction networks
* Code for plotting simple graphs from within programs
| Numerical Recipes books are excellent introductions to many
| computational problems, including linear algebra. I do agree that
| the code itself that is given in the book is sometimes not the most
| robust, but on the other hand, something like LAPACK code is robust
| but too complicated for a beginner to understand easily.
Numerical Recipes is an authoritative source of information
about a vast number of numerical procedures.
Therefore I endorse Ron Shepherd's remark that it is an
"excellent introductions to many computational problems, including linear algebra".
At more than 1200 pages, and more than 400 propgrams/procedures,
there is something there for everybody.
Three editions have been published.
________________
New in the Third Edition (2007):
* A new chapter on classification and inference,
including such topics as Gaussian mixture models,
hidden Markov modeling, hierarchical clustering
(phylogenetic trees), and support vector machines.
* A new chapter on computational geometry, including
topics like KD trees, quad- and octrees, Delaunay
triangulation and applications, and many useful
algorithms for lines, polygons, triangles, spheres, and
so on.
* Many new statistical distributions, with pdf's, cdf's,
and inverse cdf's
* An expanded treatment of ODEs, emphasizing recent
advances, and with completely new routines
* Much-expanded sections on uniform random
deviates. and on deviates from many other statistical
distributions
* An introduction to spectral and pseudospectral
methods for PDEs
* Interior point methods for linear programming
* More on sparse matrices
* Interpolation on scattered data in multidimensions
* Curve interpolation in multidimensions
* Quadrature by variable transformation, and adaptive
quadrature
* More on Gaussian quadratures and orthogonal
polynomials
* More on accelerating the convergence of series
* Improved incomplete gamma and beta functions,
and new inverse functions
* Improved spherical harmonics and fast spherical
harmonic transforms
* Generalized Fermi-Dirac integrals
* Multivariate Gaussian deviates
* Algorithms and implementations for hash memory
functions
* Incremental quantile estimation
* Chi-square with small numbers of counts
* Dynamic programming
* Hard and soft error correction, and Viterbi decoding
* Eigensystem routines for real, nonsymmetric matrices
* Multitaper methods for power spectral estimation
* Wavelets on the interval
* Information-theoretic properties of distributions
* Markov chain Monte Carlo
* Gaussian process regression and Kriging
* Stochastic simulation of chemical reaction networks
* Code for plotting simple graphs from within programs