Numerical Recipes in C: The Art of Scientific Computing

Book Description

The product of a unique collaboration among four leading scientists in academic research and industry, Numerical Recipes is a complete text and reference book on scientific computing. In a self-contained manner it proceeds from mathematical and theoretical considerations to actual practical computer routines. With over 100 new routines bringing the total to well over 300, plus upgraded versions of the original routines, the new edition remains the most practical, comprehensive handbook of scientific computing available today.

Customer Reviews:

talk about outdated.......2007-06-27

this book was likely a looker back in the day, but its 2007 now. Need to have better details for non "C"-users. wish i had bought "Idiots Guide to C".

A classic book of numerical algorithms.......2006-12-24

This book, although published 15 years ago, is still very useful. In fact, its more recent counterpart "Numerical Algorithms in C++" is a mess, and I wouldn't recommend it to anyone. The explanations of the algorithms that occur in each section of this book are top-notch. It helps with such questions as "Sure you know how to evaluate an integral with pencil and paper, but how do you do it with a computer?" Everything from linear algebra techniques to integration and evaluation of functions to the FFT and spectral applications are explained clearly and coded up in C. The code is great too, with the exception of one problem that several reviewers have already mentioned - the author has a FORTRAN-like programming style in which each implementation has arrays going from 1 to n versus 0 to n-1. This does cause some implementation problems if you want to transfer the algorithms into another programming language. Overall, though, I can't think of one book that does all of the heavy lifting that this one book does as well as it does in the arena of numerical algorithms.

The book is now available online. Just type "Numerical Recipes" into Google and click on the Numerical Recipes Home Page to peruse the entire book free of charge. You might also find the "Numerical Recipes in C Example Book" useful. That book is simply the source programs that demonstrate all of the Numerical Recipes subroutines. Each example program contains comments and is preceded by a short description of how it functions. I know I found it helpful in many cases.

Very nice book.......2006-08-27

A must buy for students or researchers who need numerical methods. Comprehensive topics. A good place to start to deeper levels. Online book is good for quick look.

A classic, and still worth having.......2006-07-12

"Numerical Recipes" has been a staple in computing libraries for many years, and for good reason. It provides immediately usable implementations of all the workhorses of numerical computation, in production-quality form. Maybe there are better implementations out there, FFTW for example, but getting something to work correctly always comes before getting it to work fast. Numerical computation is a specialty, and vanishingly few of us are specialists. As a result, getting this much specialist knowledge for the price of a very few hours' wage, fully debugged and documented, is a great bargain.

I have to agree with the critics who point out that the Gnu Scientific Library (GSL) is more complete in some areas, and offers better licensing terms. This collection has its own strengths, though, and not just in documentation. The writeup, however, is the major interface between the software and us, the bio-ware. GSL's collection of 'man' (help) pages serves a purpose, but this book's exposition describes a lot more of the background and rationale for the routines. The code and man pages are self-evident statements of the implementation - but "what" is a very different question than "what else" or "why."

This one may not serve all needs. You'd be amazed how many it does serve, though. If you need more than a Matlab session for numerical computing, you need this.

//wiredweird

Great compilation of numerical routines for C programmers.......2004-12-17

I found this book indispensible in my effort to develop profitable trading systems for futures and options and in my research in factor analysis and, more recently, in chronic fatigue syndrome and fibromyalgia. Anyone who programs in C or C++ and works with mathematics must have this book. It covers a surprisingly wide range of algorithms: routines are included for everything from handling Julian dates and solving systems of linear equations to determining eigenvectors and singular value decompositions, solving differential equations, doing numerical integration (quadrature), not to mention calculating fast fourier transforms, lomb periodograms and maximum entropy spectral analyses. While not always state-of-the-art, the routines are quite reliable (when used correctly), clearly-written, and easy to understand and use. I would strongly recommend this book (and the companion software) to anyone who programs in C and is literate in mathematics. I always keep a copy nearby.

Jeffrey Owen Katz, Ph.D.
Author: "The Encyclopedia of Trading Strategies" (McGraw Hill, 2000)

Average customer rating:

very nice conceptual overview
Not for the practitioner
Trash
Excellent Introduction, Sparse on Details
A Good Introductory Survey

Scientific Computing
Michael T. Heath
Manufacturer: The McGraw-Hill Companies, Inc.
ProductGroup: Book
Binding: Hardcover

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ASIN: 0072399104

Book Description

Heath 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.

Customer Reviews:

very nice conceptual overview.......2006-07-22

Wow, people seem to be really split on this book. I had Mike Heath for numerical analysis/scientific computing and he was an excellent instructor, one of the best lecturers I've ever had. (As a consequence, I have a hard time separating the book and the class, so judge accordingly.) The book is based on his lecture notes, though he added some material and didn't cover every topic in the book. Just reading the book is useful to give you an overview of the point behind different methods. The goal of the class for which this book was written is actually quite conceptual. It was to give scientists (that's me: a stats researcher who makes heavy use of numerical computation) and CS people in areas other than scientific computing a leg up. It was only a first class for people in scientific computing, the rough equivalent of intro Physics or intro Probability/Stats for people in those respective majors. However, you *won't* be prepared to "roll your own" from this book. In fact, at the beginning of the semester Heath was very careful to note that if you have the opportunity to use a library function for most numerical programming, you are nuts to roll your own. Why? Numerical algorithms are usually extremely complicated and the authors of the code often spend years developing careful expertise on them. Frequently the formulas used to elucidate a given method are NOT the ones used to implement it. You need error traps, tricks to handle ill-scaling and other special cases, etc. These are things that someone who has a one-semester, superficial understanding of a topic simply won't have. So consider the book on the goals it set: it is an overview of a field. If you want to learn more about any one topic, you have to dig deeper and consult references and other works, but this is a good place to start. For this, the book serves admirably.

Not for the practitioner.......2005-11-17

If you are interested in Scientific computing from the viewpoint of the end user that is the guy who uses the method to solve practical engineering problems then this book is lacking.

Not enough methods in this book to constitute an introductory survey of the field. Every chapter gets heavy dose mathematical treatment, apparently Heath loves his math but for the rest of us it doesnt translate into know-how. Know how to solve equations using computational techniques. Very few derivations to back his mathematical swagger, very few examples (if any) and fewer numerical schemes to solve problems. Many of the chapters receive cursory treatment such as PDE's get about 70 pages of print. Far too little to do anyone any good.

He does talk about interesting issues such as conditioning and error analysis and computer precision and memory issues but it is done from such a superficial viewpoint that one cannot use anything to improve ones code. Not recommended if you want to learn numerical methods even if you have an excellent professor to learn from. His chapter on FFT's was even more abstruse and there was hardly any methods with which to solve PDE's.

I had this for a graduate course in Numerical Methods but ended up using Hoffman's excellent book on Numerical Methods.

Trash.......2005-10-14

If you want to have a solid understanding of numerical computation, this book is definitely the last choice. Many theorems are given without any proof or even intuitions behind them in this book. Even when a proof is provided, it's often far from rigorous. The organization of chapters is the worst I have ever seen, revelant materials are scattered over several different locations rather than put together. Take the SVD for example, it is mentioned in the end of chapter 3, but reappears in chapter 4, which is very confusing. If you are new to this area, please don't read this book. It gives you many many facts without explanations, which I think is not a good way to learn new things. David S. Watkins' Fundamentals of Matrix Computations is a lot better and easier to understand. It also emcompasses many detailed treatments of various theorems. If you have bought Heath's book, don't be sad, at least it can serve as a coaster.

Excellent Introduction, Sparse on Details.......2004-11-20

While sparse on the details of many of the algorithms and theorems mentioned, as an introduction it covers a broad range of material-enough for two semesters of study. The writing is lucid, and when a proof of a theorem is given, it is easy to follow and explained in english afterward. Rationale is given for everything, which is a great benefit to a student not familiar with the nuances of sophisticated linear algebra.

A Good Introductory Survey.......2002-11-05

This book excels at presenting a reader with little to no knowledge in computer science and a mild mathematical background (knowledge of differential equations as a prerequisite) with the fundamental concepts regarding scientific computing. The presentation of pseudo-code algorithms helps smooth the transition from analytical (pencil and paper) thinking to numerical thinking. The algorithms are presented in a manner such tha anyone with access to dozens of possible environments can apply them, though they are by no means complete, thus requiring some thought into the processes. The material covered is 110% of what an engineer will want to know, 90% of what an applied mathematician will want to know, and 45% of what a numerical analyist will want to know. In all, a great book to begin a foray into numerical computing.

Engineering and Scientific Computing with Scilab

Average customer rating:

An excellent book for scientists and engineers
Fine book on an excellent software

Engineering and Scientific Computing with Scilab

Manufacturer: Birkhäuser Boston
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ASIN: 0817640096

Customer Reviews:

An excellent book for scientists and engineers.......2006-02-10

The book "Engineering and Scientific Computing" in Scilab,
presents clearly the elements of the Scilab language.
A scientist with some programming background,
even elementary, can readily learn and exploit the
elegant and compact Scilab scientific programming
environment.
However, the strongest point of the book is its tutorial
value. The reader can through the Scilab tool,
improve the knowledge of important signal processing topics,
exploit algorithms for the numerical solution of ODEs and
tackle with optimization problems. Furthermore, the book contains
excellent material on SCICOS (the dynamical system builder
that accompanies Scilab) and a lot of applications.
The CD that is included with the book is also very helpful.
The presented scientific applications are aimed mainly
to the advanced scientists and engineers, although a large part
can be utilized and by undergraduate students of an intermediate
level.
In summary, I liked and enjoyed this book and I strongly
recommend it to all the scientific/engineering community.

Fine book on an excellent software.......2001-07-11

This is a good book describing an excellent free scientific Matlab-like software package available for many computing platforms. It complements well the extensive on-line help of the software and the information available on the Web.

The first three chapters gives a condensed overview of the software. I found the description of the graphics capabilities particularly useful as a reference. The next two chapters describe the use of the software for linear algebra, polynomials, linking to C and FORTRAN, and more advanced aspects. The remaining chapters concern tools and applications mainly of a system oriented nature. The tools are generally of a very high quality and accuracy, but of course slower than in compiled languages.

Book Description

Scientific Computing and Differential Equations: An Introduction to Numerical Methods, is an excellent complement to Introduction to Numerical Methods by Ortega and Poole. The book emphasizes the importance of solving differential equations on a computer, which comprises a large part of what has come to be called scientific computing. It reviews modern scientific computing, outlines its applications, and places the subject in a larger context.
This book is appropriate for upper undergraduate courses in mathematics, electrical engineering, and computer science; it is also well-suited to serve as a textbook for numerical differential equations courses at the graduate level.

* An introductory chapter gives an overview of scientific computing, indicating its important role in solving differential equations, and placing the subject in the larger environment
* Contains an introduction to numerical methods for both ordinary and partial differential equations
* Concentrates on ordinary differential equations, especially boundary-value problems
* Contains most of the main topics for a first course in numerical methods, and can serve as a text for this course
* Uses material for junior/senior level undergraduate courses in math and computer science plus material for numerical differential equations courses for engineering/science students at the graduate level

Customer Reviews:

Does the job well.......2001-06-30

This book is an excellent introduction to the field of scientific computing and serves well as a textbook, given the many exercises included in it. Although the software packages quoted in the book have been considerably revised since the time of publication of the book, one can still use it effectively as a guide to the construction of algorithms and software for scientific applications. The level of the book makes it suitable for a course in numerical analysis at the advanced undergraduate level. After a brief review of the concepts and strategies employed in mathematical modeling in chapter 1, the author begins in chapter 2 with the study of initial value problems for ordinary differential equations. He motivates the discussion with the predator-prey problem from mathematical biology and the ballistic trajectory problem with air resistance from physics. The initial-value problem for the general case of systems of ordinary differential equations is then solved using finite difference methods. The author treats thoroughly Euler's method along with its discretization error. Recognizing that first-order methods have very slow rates of convergence, Runge-Kutta methods are discussed next to alleviate this problem. The Heun method, fourth-order method, and more general one-step methods are discussed in detail. The sample initial value problems are then treated using some of these techniques. The technique of polynomial interpolation, so popular as a solution technique in high-level symbolic programming languages such as Mathematica, is discussed in this chapter also. Multistep methods, such as the Adams-Bashforth, Adams-Moulton, and predictor-corrector methods are treated also. The author also discusses the important concept of stability in this chapter. Although he does not give a rigorous definition of stability, due to the mathematical formalism needed for such a definition, he does give several examples of differential equations that are not stable, and also examples of instabilities in the actual numerical methods employed.

Boundary value problems for ordinary differential equations are treated in the next chapter. The author motivates the problem via a two-point boundary value problem, but only concentrates on linear boundary value problems in this chapter, with the nonlinear case treated in chapter 5. The author carefully distinguishes between Dirichlet and Neumann boundary conditions. The solution of the discretized problem is solved appropriately with Gaussian elimination, and the author gives a numerical example. The case of periodic boundary conditions is also treated, and the author chooses to solve the resulting linear system using the Sherman-Morrison technique, instead of Gaussian elimination, arguing (correctly) that this method only needs code for solving tridiagonal matrices.

The study of the solution of linear systems of equations is taken up in more detail in the next chapter, with emphasis on solution techniques for banded or full matrices. The class of least square problems is treated first, with least square polynomials and their calculation using orthogonal polynomials. The author then treats Gaussian elimination in more detail in this chapter, with treatments of LU factorization and banded matrices being treated. The author gives the reader more details on the performance issues involved in the different solution techniques. Ill-conditioning and error analysis are first discussed here in the context of solution of systems of linear equations, along with definitions and calculations of condition numbers. The author also gives good overviews of alternative factorization techniques, such as Cholesky and QR factorization.

The most important application of numerical methods is in the class of nonlinear problems, since these usually do not have analytical solutions. Even if analytical solutions are found in terms of special functions, the calculation of these special functions typically must be done using techniques from numerical analysis. Nonlinear problems are discussed in chapter 5 of this book, wherein the author again uses the projectile problem to introduce shooting methods. This is followed by a very detailed discussion of the solution of a nonlinear equation using bisection, secant, and Newton's methods. Systems of nonlinear equations are then discussed, with the infamous Picard iteration technique leading the discussion, followed again by a treatment using Newton's method.

Then in the next chapter, the author switches gears somewhat by moving away from techniques based on finite differences and discussing ones such as finite element, Galerkin's and Rayleigh-Ritz methods. The mathematical considerations employed in this chapter are a little more involved than the other chapters, but the author explains the ideas well, and the assigned exercises shed more light on the issues involved. Spine approximations are also discussed, along with the numerical evaluation of the integrals that naturally arise in Galerkin methods.

Eigenvalue problems, so ubiquitous in all areas of science and engineering, are the subject of the next chapter. Interestingly, the author discusses Gerschgorin's theorem, which usually does not appear in a book at this level. Most of the popular techniques for solving eigenvalue problems, such as QR and iterative methods, are discussed thoroughly.

The author gives the reader a taste of the numerical solution of partial differential equations starting in the next chapter, where the heat equation, wave equation, and Poisson's equation lead off the discussion. Separation of variables is discussed briefly as a technique of solution, but the author places emphasis first on finite difference methods for solving these equations. The stability and error analysis of these methods are first studied for the heat and wave equations, and this is followed by a discussion of implicit methods, with a brief treatment given of the Crank-Nicolson method. After a discussion of semi-discrete methods, the author then moves on in last chapter to problems in dimensions two and three. The sparse matrices arising from the discretization of the problems are shown for the Poisson and heat equations. The ADI method, along with Gaussian elimination, Jacobi's, Gauss-Seidel, SOR, and conjugate gradient methods are all given fine treatments.

Python Scripting for Computational Science (Texts in Computational Science and Engineering)
Average customer rating:

strong computational emphasis

Great Suppliment to Numerical methods

Python for Science Academics and Engineers, NOT programmers

Convincing demonstration of Python's value in science

Python Scripting for Computational Science (Texts in Computational Science and Engineering)
Hans Petter Langtangen
Manufacturer: Springer
ProductGroup: Book
Binding: Hardcover

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Book Description

The goal of this book is to teach computational scientists how to develop tailored, flexible, and human-efficient working environments built from small programs (scripts) written in the easy-to-learn, high-level language Python. The focus is on examples and applications of relevance to computational scientists: gluing existing applications and tools, e.g. for automating simulation, data analysis, and visualization; steering simulations and computational experiments; equipping old programs with graphical user interfaces; making computational Web applications; and creating interactive interfaces with a Maple/Matlab-like syntax to numerical applications in C/C++ or Fortran. In short, scripting with Python makes you much more productive, increases the reliability of your scientific work and lets you have more fun - on Unix, Windows and Macintosh. All the tools and examples in this book are open source codes. The third edition is compatible with the new NumPy implementation and features updated information, correction of errors, and improved associated software tools.

Customer Reviews:

strong computational emphasis.......2006-11-13

Langtangen's emphasis here is on a reader who comes from a strong background in engineering or science, and is familiar with common computational ideas and has done some programming, but not necessarily in Python. The typical book on Python is aimed at a general programming reader, and the examples in such a book usually are quite elementary, from a computational viewpoint.

The merit of Langtangen's book is that he gets into a lot of computational ideas. This is not a trivial book. Aspects like parsing data in files, connecting to local and remote hosts, and interacting with programs written in other languages are covered. For the latter, the important cases of Fortran and C programs are explained. The choices of these languages is deliberate. In science and engineering, they are the dominant languages for raw computation. And you are likely to have legacy code written in these, that you cannot abandon while using Python.

Great Suppliment to Numerical methods.......2006-07-25

When I first got ahold of this book I had just finished learning all the gory details of good numerical codes. But when developing tests for simple cases I found that development went way too slow, so someone suggested I learn Python. This book provides a great demonstration of how python can supplement your existing codes. Either by organizing the tests, formatting output, or just adding pretty interfaces.

This book contains a lot of the necessary extras that a scientist or engineer must do to get his work going or finished, which is too pedantic to be taught in most courses. It shows the power of Python over some other scripting languages for this purpose. It is definitely one of the best references on my book shelf.

Python for Science Academics and Engineers, NOT programmers.......2005-06-03

I bought this book as an experienced programmer and Unix user expecting more of a "Numerical Recepies in Python" emphasis on the efficient implementation of algorithms which happen to be in Python. I should have paid more attention to the description.

This book is really more of a "Grad Student's Guide to Everyday Python Usage". I imagine it would be very valuable to a mathematics Grad student without too much programming or shell experience, looking for an alternative to Matlab. However, there is very little "Computational Science" in this book. Do NOT expect a cookbook of high performance algorithm implementations.

The book is a very verbose 700+ pages, all in an unexciting academic LaTeX format. The author works through idiom after idiom for accomplishing different tasks in fairly stand-alone sub-sections without much of a feeling of conceptual "flow" between them. It sort of feels like reading through the author's personal lab notes that he took everytime he learned a new language feature or trick.

If you are an experienced programmer, you will quickly get impatient with the verbose presentation that emphasizes idioms and examples instead of fundamental concepts and syntax reference tables. But, if you are an experienced programmer, you are not the target audience for this book.

Braddock Gaskill

Convincing demonstration of Python's value in science.......2004-10-15

The author has 2 main goals:
1) To improve the productivity of scientists familiar with specific software systems (especially Matlab, Maple, and Mathematica) by teaching them to "glue" applications together.
2) To advocate Python as the preferred "glue" language. In his own words, "I hope to convince computational scientists having experience with Perl that Python is a preferable alternative, especially for large long-term projects."

He has certainly done a creditable job. As an expert in computational differential equations, he neglects neither efficiency nor correctness, while stressing both simplicity and reliability. In this sense, he has done a great service to the Python community.

The question is: What justifies the purchase of his book?
The answer is: Chapters 4, 9, and 10.

Contents:

1. Introduction--26pp
Very convincing arguments.

2. Getting Started With Python Scripting--38pp
Interesting examples.

3. Basic Python--56pp
A too-quick tutorial. Go to python dot org instead.

4. Numerical Computing in Python--48pp
Stellar explanations of vectorized array operations.

5. Combining Python with Fortran, C, and C++--36pp
Details use of Fortran2Py and SWIG. Mentions many alternatives.

6. Introduction to GUI Programming--70pp
Useful examples of Tkinter/pmw widgets.

7. Web Interfaces and CGI Programming--24pp
Good source of ideas.

8. Advanced Python--132pp
Deep and extensive. Includes: option parsing, regular expressions, data persistence and compression, object-oriented programming, exceptions, generic programming, efficiency.

9. Fortran Programming with NumPy Arrays--32pp
All about efficiency and re-use.

10. C and C++ Programming with NumPy Arrays--40pp
More about efficiency. NumPy C API, C++ objects, and SCXX.

11. More Advanced GUI Programming--73pp
Tedious discussion of both Web and standalone GUIs. BLT, canvas, cgi.

12. Tools and Examples--70pp
Excellent examples of PDE solvers, with a powerful GUI, but quite long and tedious.

A. Setting up the Required Software Environment--16pp
Wonderfully specific installation instructions!

B. Elements of Software Engineering--50pp
Python's strength! Very practical advice on modularity, documentation, coding style, regression-testing, version-control.

Strengths:
+ Downloadable py4cs package, esp. numpytools module
+ Great advice everywhere, e.g. CGI checklist, Pythonic programming, and trouble-shooting.
+ Concrete evidence for most assertions.
+ Very attractive presentation. Sturdy, high-quality cover, binding and pages. Brief, elegant code fragments (except in Chapter 12). Readable prose. No wasted space.
+ Available as 5MB pdf file, after purchase of hardcopy. Very nice.
+ Slides, installation instructions, and errata also at web site. Very professional.

My peeves:
- Not enough tables to be a useful manual.
- On p.428(#7) he points out that handling a raised exception is very slow. However, when I time his example with a positive argument, the try-except version is 20% faster (b/c the if clause is skipped), so he is actually giving bad advice for the general case. Luckily, he contradicts himself later, on page 685: "Exceptions should be used instead of if-else tests." The best advice: Avoid common exceptions in inner loops.
- The 10-page index is not as great as it at first seems. (See Martelli's Python in a Nutshell for a better one.)
- Pure interface functions should 'raise NotImplementedError', rather than 'return'.
- Exceptions should never be trapped mindlessly with 'except:'. That would hide your own SyntaxErrors!
- Too many exercises. (It's published as a textbook.) Since there are no answers, the exercises are useless for non-students. (See Lutz's Learning Python for effective exercises with answers.)

Overall rating:
This contains the best information on numerical programming in Python that I've seen. Though expensive, it could easily be your only Python book, given the excellent online documenation already available.

Numerical Recipes in C Example Book: The Art of Scientific Computing

Average customer rating:

Handy companion for Numerical Recipes in C
Another half for "Numerical Recipes in C"

Numerical Recipes in C Example Book: The Art of Scientific Computing
William H. Press , Brian P. Flannery , Saul A. Teukolsky , and William T. Vetterling
Manufacturer: Cambridge University Press
ProductGroup: Book
Binding: Paperback

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ASIN: 0521437202

Book Description

These example books published as part of the Numerical Recipes, Second Edition series are source programs that demonstrate all of the Numerical Recipes subroutines. Each example program contains comments and is prefaced by a short description of how it functions. The books consist of all the material from the original edition as well as new material from the Second Edition. They will be valuable for readers who wish to incorporate procedures and subroutines into their own source programs. They are available in Fortran, C, and C++.

Customer Reviews:

Handy companion for Numerical Recipes in C.......2007-03-09

If you need to see programming examples on how to use the numerical algorithms that have been transformed into C code in Press' classic Numerical Recipes in C, then this is a very handy little book. Every chapter in this book has a one-to-one correspondence with the chapters in Numerical Recipes. The code is clear and well-commented. However, there is one draw-back. The code in this book is written in the same annoying Fortran style as Press' other book. Press codes like a Fortran programmer who just won't give up the ship and do things like start his arrays at zero instead of one. However, assuming you have waded through Numerical Recipes prior to this, understanding his programming style is one of the skills you should have already mastered. Just remember there is nothing complex going on in this book. You are just being shown examples on how to use the subroutines in the other book. If you took the trouble to go through Numerical Recipes in the first place, it is likely you already know how you intend to use the subroutines it contains.

Another half for "Numerical Recipes in C".......1997-03-09

If you just see Numerical Recipes ,you will annoy the exact usage of codes. It is the practical book for the usage of codes in "Numerical Recipes". For real world, this is more helpful than "Numerical Recipes"

Numerical Recipes in FORTRAN Example Book: The Art of Scientific Computing

Average customer rating:

A great recipe

Numerical Recipes in FORTRAN Example Book: The Art of Scientific Computing
William H. Press , Brian P. Flannery , Saul A. Teukolsky , and William T. Vetterling
Manufacturer: Cambridge University Press
ProductGroup: Book
Binding: Paperback

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Similar Items:

Numerical Recipes in FORTRAN: The Art of Scientific Computing
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ASIN: 0521437210

Book Description

The example books published as part of the Numerical Recipes, Second Edition series are source programs that demonstrate all of the Numerical Recipes subroutines. Each example program contains comments and is prefaced by a short description of how it functions. The books consist of all of the material from the original edition, as well as new material from the Second Edition. They will be valuable for readers who wish to incorporate procedures and subroutines into their own source programs. They are available in both Fortran and C.

Customer Reviews:

A great recipe.......2001-05-10

This is really a great book of numerical examples. It teaches you on how to use those "abstract" numerical recipes. From here you will be a good commander of both numerical recipes and numerical programming. You will learn a lot of practical experience. You can not miss it! enjoy it.

Numerical Methods for Partial Differential Equations (Computer Science and Scientific Computing)

Average customer rating:

Very good and helpful book for the engineer with fundamental

Numerical Methods for Partial Differential Equations (Computer Science and Scientific Computing)
William F. Ames
Manufacturer: Academic Press
ProductGroup: Book
Binding: Hardcover

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Book Description

This volume is designed as an introduction to the concepts of modern numerical analysis as they apply to partial differential equations. The book contains many practical problems and their solutions, but at the same time, strives to expose the pitfalls--such as overstability, consistency requirements, and the danger of extrapolation to nonlinear problems methods used on linear problems. Numerical Methods for Partial Differential Equations, Third Edition reflects the great accomplishments that have taken place in scientific computation in the fifteen years since the Second Edition was published. This new edition is a drastic revision of the previous one, with new material on boundary elements, spectral methods, the methods of lines, and invariant methods. At the same time, the new edition retains the self-contained nature of the older version, and shares the clarity of its exposition and the integrity of its presentation.

Key Features
* Material on finite elements and finite differences have been merged, and now constitute equal partners
* Additional material has been added on boundary elements, spectral methods, the method of lines, and invariant methods
* References have been updated, and reflect the additional material
* Self-contained nature of the Second Edition has been maintained
* Very suitable for PDE courses

Customer Reviews:

Very good and helpful book for the engineer with fundamental.......2005-02-16

This book is very detail on how to generate numerical methods for partial differential equations. Staring from basics, the author proceeds with detailed examples and more complicated ideas. This is book will be very helpful for the people having basic computational knowledge and scientific computing experience.

But the book is written in 1977. Some difficult ideas will be easier to be implemented on large scale computers such as Linux clusters or SGI machines. I also found one small shortcoming is that the problem complexity is not detailed discussed. This will be very important now for comparing different algorithms to accomplish the same problem.

Classical and Quantum Computing: with C++ and Java Simulations

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Great Book .. a must have for the serious computer scientest
simulations improve understanding

Classical and Quantum Computing: with C++ and Java Simulations
Yorick Hardy , and Willi H. Steeb
Manufacturer: Birkhäuser Basel
ProductGroup: Book
Binding: Paperback

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Problems And Solutions in Quantum Computing And Quantum Information
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ASIN: 3764366109

Book Description

"Classical and Quantum computing" provides a self-contained, systematic and comprehensive introduction to all the subjects and techniques important in scientific computing. The style and presentation are readily accessible to undergraduates and graduates. A large number of examples, accompanied by complete C++ and Java code wherever possible, cover every topic.

Features and benefits:
- Comprehensive coverage of the theory with many examples
- Topics in classical computing include boolean algebra, gates, circuits, latches, error detection and correction, neural networks, Turing machines, cryptography, genetic algorithms
- For the first time, genetic expression programming is presented in a textbook
- Topics in quantum computing include mathematical foundations, quantum algorithms, quantum information theory, hardware used in quantum computing

This book serves as a textbook for courses in scientific computing and is also very suitable for self-study. Students, professionals and practitioners in computer science, applied mathematics and physics will benefit from using the book and the included software simulations.

Customer Reviews:

Great Book .. a must have for the serious computer scientest.......2006-07-29

This book is easy to read, and easy to follow with a wealth of information and hands on exaqmples that put you in the context in the right manner leading you to a better understanding of the subject matter. The author goes all the way from classical computing and its digital logic to the quantum computing world with examples in C++ that help concieve the inherent massive parallelism of Quantum Computers.

Comparing this title against others, this does stand out as a favorite for me. The ability of the authers to start from the classical and drive the reader to the quantum world flawlessly truely stands out; especially with C++ simulations described in the book. Two thumbs up indeed.

simulations improve understanding.......2005-04-11

The topics discussed are quite advanced. Typically these are covered in very theoretical books, that describe high level abstractions, with little in the way of code for the reader to experiment with. Hardy offers a different approach. There is still an advanced treatment of the topics. Where these range from cryptography to genetic methods to quantum computing and others.

But he gives you C++ and Java code, to simulate various systems. A great benefit in aiding your understanding of what are often research level matters. In which case, if you are a new researcher, like a grad student perhaps, then you might be able to get up to speed quicker.

An Introduction to Scientific Computing: Twelve Computational Projects Solved with MATLAB (Texts in Applied Mathematics)

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An Introduction to Scientific Computing: Twelve Computational Projects Solved with MATLAB (Texts in Applied Mathematics)
Ionut Danaila , Pascal Joly , Sidi Mahmoud Kaber , and Marie Postel
Manufacturer: Springer
ProductGroup: Book
Binding: Hardcover

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ASIN: 038730889X

Book Description

This book provides twelve computational projects aimed at numerically solving problems from a broad range of applications including Fluid Mechanics, Chemistry, Elasticity, Thermal Science, Computer Aided Design, Signal and Image Processing. For each project the reader is guided through the typical steps of scientific computing from physical and mathematical description of the problem, to numerical formulation and programming and finally to critical discussion of numerical results. Considerable emphasis is placed on practical issues of computational methods. The last section of each project contains the solutions to all proposed exercises and guides the reader in using the MATLAB scripts. The mathematical framework provides a basic foundation in the subject of numerical analysis of partial differential equations and main discretization techniques, such as finite differences, finite elements, spectral methods and wavelets).

The book is primarily intended as a graduate-level text in applied mathematics, but it may also be used by students in engineering or physical sciences. It will also be a useful reference for researchers and practicing engineers.

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