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Interdisciplinary Computing in Java Programming [Hardcover]

Sun-Chong Wang
5.0 out of 5 stars  See all reviews (2 customer reviews)
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Book Description

Aug. 31 2003 The Springer International Series in Engineering and Computer Science (Book 743)

Books on computation in the marketplace tend to discuss the topics within specific fields. Many computational algorithms, however, share common roots. Great advantages emerge if numerical methodologies break the boundaries and find their uses across disciplines. Interdisciplinary Computing In Java Programming Language introduces readers of different backgrounds to the beauty of the selected algorithms. Serious quantitative researchers, writing customized codes for computation, enjoy cracking source codes as opposed to the black-box approach. Most C and Fortran programs, despite being slightly faster in program execution, lack built-in support for plotting and graphical user interface. This book selects Java as the platform where source codes are developed and applications are run, helping readers/users best appreciate the fun of computation.

Interdisciplinary Computing In Java Programming Language is designed to meet the needs of a professional audience composed of practitioners and researchers in science and technology. This book is also suitable for senior undergraduate and graduate-level students in computer science, as a secondary text.


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Most helpful customer reviews
Format:Hardcover
The book covers a great number of algorithms some of which, such as the Metropolis algorithm, are embedded within a chapter. Applicability of the algorithms are amazingly broad as evidenced from the topics of the written examples in each chapter. Encyclopedic, this book is a must-have for any serious computational researcher.
The first 3 chapters of the book introduce Java language. Topics include Java basics, graphics, threads and distributed computing via RMI. The level is intermediate to advanced. Each chapter contains a working example: chapter 1 lists a matrix class; chapter 2 a Java GUI; and chapter 3 an RMI implementation.
The 2nd part of the book is focused on various computational algorithms. Chapter 4 is on Simulated Annealing which is powerful for optimization. The example application is minimizing the free energy of a 3-dimensional Ising lattice.
Chapter 5 is on artificial neural network which is useful for classification. The text includes some tips on stock index prediction using neural network. The example application of this chapter is a Kohonen self-organizing feature map for clustering.
Chapter 6 is on Genetic Algorithm that is inspired from Darwinian evolution. The example application is the canonical "Traveling Salesman problem" in optimization.
Chapter 7 is the cellular automata that have been used to simulate natural as well as social phenomena. The example application of this chapter is a 2-dimensional fluid flow through obstructions.
Chapter 8 is Monte Carlo method that is used in all kinds of simulations The example application is modeling the drift-diffusion behavior of a stock price.
Chapter 9 is Molecular Dynamics which is widely used in chemistry and molecular biology for simulations such as protein folding.
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Was this review helpful to you?
5.0 out of 5 stars an academic yet practical book Feb. 5 2004
Format:Hardcover
It collects state-of-the-art computational algorithms that are amenable to multidisciplinary applications.
A website containing its chapter examples in Java applet can be found at,
[...]
Look at the lively demonstrations there to convince yourself.
Was this review helpful to you?
Most Helpful Customer Reviews on Amazon.com (beta)
Amazon.com: 3.7 out of 5 stars  3 reviews
2 of 2 people found the following review helpful
5.0 out of 5 stars A must-have for any serious computational researcher Nov. 19 2003
By E. E. H. - Published on Amazon.com
Format:Hardcover
The book covers a great number of algorithms some of which, such as the Metropolis algorithm, are embedded within a chapter. Applicability of the algorithms are amazingly broad as evidenced from the topics of the written examples in each chapter. Encyclopedic, this book is a must-have for any serious computational researcher.
The first 3 chapters of the book introduce Java language. Topics include Java basics, graphics, threads and distributed computing via RMI. The level is intermediate to advanced. Each chapter contains a working example: chapter 1 lists a matrix class; chapter 2 a Java GUI; and chapter 3 an RMI implementation.
The 2nd part of the book is focused on various computational algorithms. Chapter 4 is on Simulated Annealing which is powerful for optimization. The example application is minimizing the free energy of a 3-dimensional Ising lattice.
Chapter 5 is on artificial neural network which is useful for classification. The text includes some tips on stock index prediction using neural network. The example application of this chapter is a Kohonen self-organizing feature map for clustering.
Chapter 6 is on Genetic Algorithm that is inspired from Darwinian evolution. The example application is the canonical "Traveling Salesman problem" in optimization.
Chapter 7 is the cellular automata that have been used to simulate natural as well as social phenomena. The example application of this chapter is a 2-dimensional fluid flow through obstructions.
Chapter 8 is Monte Carlo method that is used in all kinds of simulations The example application is modeling the drift-diffusion behavior of a stock price.
Chapter 9 is Molecular Dynamics which is widely used in chemistry and molecular biology for simulations such as protein folding. The example code of this chapter is evaporation of a 3-dimensional gas.
Chapter 10 is Feynman's path integral. This chapter is a bit technical, requiring background in quantum mechanics. The example application is the pricing of financial options.
Chapter 11 is chi-square fits which is a chore in any data-analysis. The author rewrites a legacy Fortran chi-square fitting routine into a Java class.
Chapter 12 is Bayesian analysis which has recently gained popularity because of advances in computing power. The application code of this chapter is Pixon algorithm in imagine restoration.
Chapter 13 is about Graph which is related to Bayesian method. The example class of this chapter is Kalman algorithm that has been used in real-time projectile (such as missile) tracking.
Appendix is about web-computing, achieved by converting standalone applications in previous chapters into Java applets.
1 of 1 people found the following review helpful
5.0 out of 5 stars an academic yet practical book Feb. 5 2004
By Frank Wang - Published on Amazon.com
Format:Hardcover
It collects state-of-the-art computational algorithms that are amenable to multidisciplinary applications.
A website containing its chapter examples in Java applet can be found at,
[...]
Look at the lively demonstrations there to convince yourself.
0 of 3 people found the following review helpful
1.0 out of 5 stars Don't waste your time or money with this book! April 3 2009
By WDP - Published on Amazon.com
Format:Hardcover
I would gladly have given this book 5 stars BUT it does not provide readers with access to the source code for the Java program examples in the book and so this book is totally worthless unless you have the time and want to spend zillions of hours typing the code into a computer yourself. People today just don't have the luxury of time to spend typing in a zillion lines of code themselves! By the way, Frank Wang's website given in another commentary on this page: [...]
is completely useless as it contains very few applets and they are all in execute-only format. I tried contacting the author by email but never got any response. So my advice is this: don't waste your money buying this book unless you have zillions of hours to spend typing all that source code into a computer yourself.
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