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.
I Java Language.- 1. Java Basics.- 1.1 Object Oriented Programming.- 1.2 An Object Example.- 1.3 Primitive Data Types.- 1.4 Class Constructor.- 1.5 Methods of a Class.- 1.6 Exceptions.- 1.7 Inheritance.- 1.8 Usage of the Matrix Class.- 1.9 Running the Program.- 1.10 Summary.- 1.11 References and Further Reading.- 2. Graphical And Interactive Java.- 2.1 Windowed Programming.- 2.2 Example of a Window Object.- 2.3 Frame.- 2.4 Panel.- 2.5 Menu.- 2.6 Interactions.- 2.7 File Input/Output.- 2.8 StreamTokenizer.- 2.9 Graphics.- 2.10 Printing.- 2.11 Summary.- 2.12 References and Further Reading.- 3. High Performance Computing.- 3.1 Parallel Computing.- 3.2 Java Threads.- 3.3 An Example of Parallel Computing.- 3.4 Distributed Computing.- 3.5 Remote Method Invocation.- 3.6 An RMI Client.- 3.7 The Remote Interface.- 3.8 Serialization.- 3.9 A Reflective RMI Server.- 3.10 Reflection.- 3.11 Build and Run the Server.- 3.12 Build and Run the Client.- 3.13 Summary.- 3.14 Appendix.- 3.15 References and Further Reading.- II Computing.- 4. Simulated Annealing.- 4.1 Introduction.- 4.2 Metropolis Algorithm.- 4.3 Ising Model.- 4.4 Cooling Schedule.- 4.5 3-Dimensional Plot and Animation.- 4.6 An Annealing Example.- 4.7 Minimization of Functions of Continuous Variables.- 4.8 Summary.- 4.9 References and Further Reading.- 5. Artificial Neural Network.- 5.1 Introduction.- 5.2 Structural vs. Temporal Pattern Recognition.- 5.3 Recurrent Neural Network.- 5.4 Steps in Designing a Forecasting Neural Network.- 5.5 How Many Hidden Neurons/Layers ?.- 5.6 Error Function.- 5.7 Kohonen Self-Organizing Map.- 5.8 Unsupervised Learning.- 5.9 A Clustering Example.- 5.10 Summary.- 5.11 References and Further Reading.- 6. Genetic Algorithm.- 6.1 Evolution.- 6.2 Crossover.- 6.3 Mutation.- 6.4 Selection.- 6.5 Traveling Salesman Problem.- 6.6 Genetic Programming.- 6.7 Prospects.- 6.8 Summary.- 6.9 References and Further Reading.- 7. Monte Carlo Simulation.- 7.1 Random Number Generators.- 7.2 Inverse Transform Method.- 7.3 Acceptance-Rejection.- Error Estimation.- 7.5 Multivariate Distribution with a Specified Correlation Matrix.- 7.6 Stochastic-Volatility Jump-Diffusion Process.- 7.7 A Cash Flow Example.- 7.8 Variance Reduction Techniques.- 7.9 Summary.- 7.10 References and Further Reading.- 8. Molecular Dynamics.- 8.1 Computer Experiment.- 8.2 Statistical Mechanics.- 8.3 Ergodicity.- 8.4 Lennard-Jones Potential.- 8.5 Velocity Verlet Algorithm.- 8.6 Correcting for Finite Size and Finite Time.- 8.7 An Evaporation Example.- 8.8 Summary.- 8.9 References and Further Reading.- 9. Cellular Automata.- 9.1 Complexity.- 9.2 Self-Organized Criticality.- 9.3 Simulation by Cellular Automata.- 9.4 Lattice Gas Automata.- 9.5 A Hydrodynamic Example.- 9.6 Summary.- 9.7 References and Further Reading.- 10. Path Integral.- 10.1 Feynman’s Sum Over Histories.- 10.2 Numerical Path Integration and Feynman-Kac Formula.- 10.3 Options in Finance.- 10.4 A Path Integral Approach to Option Pricing.- 10.5 Importance Sampling (Metropolis-Hastings algorithm).- 10.6 Implementation.- 10.7 Summary.- 10.8 References and Further Reading.- 11. Data Fitting.- 11.1 Chi-Square.- 11.2 Marquardt Recipe.- 11.3 Uncertainties in the Best-Fit Parameters.- 11.4 Arbitrary Distributions by Monte Carlo.- 11.5 A Surface Fit Example.- 11.6 Summary.- 11.7 References and Further Reading.- 12. Bayesian Analysis.- 12.1 Bayes Theorem.- 12.2 Principle of Maximum Entropy.- 12.3 Likelihood Function.- 12.4 Image/Spectrum Restoration.- 12.5 An Iterative Procedure.- 12.6 A Pixon Example.- 12.7 Summary.- 12.8 References and Further Reading.- 13. Graphical Model.- 13.1 Directed Graphs.- 13.2 Bayesian Information Criterion.- 13.3 Kalman Filter.- 13.4 A Progressive Procedure.- 13.5 Kalman Smoother.- 13.6 Initialization of the Filter.- 13.7 Helix Tracking.- 13.8 Buffered I/O.- 13.9 The Kalman Code.- 13.10H Infinity Filters.- 13.11Properties of H Infinity Filters.- 13.12Summary.- 13.13References and Further Reading.- 14. Jni Technology.- 14.1 Java Native Interface.- 14.2 JNI How-To.- 14.3 Call Fortran Programs from C.- 14.4 A JNI Example.- 14.5 Summary.- 14.6 References and Further Reading.- Appendices.- A.- A.1 Web Computing.- A.2 Class Sources.- Inde.
