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.