Interdisciplinary Computing in Java Programming

By: Sun-Chong Wang

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Hardcover | 31 August 2003

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282 Pages

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24.77 x 17.15 x 3.18

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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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Published: 30th October 2012

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Preface	p. xi
Java Language
Java Basics	p. 3
Object Oriented Programming	p. 3
An Object Example	p. 4
Primitive Data Types	p. 8
Class Constructor	p. 9
Methods of a Class	p. 9
Exceptions	p. 10
Inheritance	p. 10
Usage of the Matrix Class	p. 11
Running the Program	p. 13
Summary	p. 14
References and Further Reading	p. 15
Graphical and Interactive Java	p. 17
Windowed Programming	p. 17
Example of a Window Object	p. 18
Frame	p. 23
Panel	p. 23
Menu	p. 24
Interactions	p. 24
File Input/Output	p. 24
Stream Tokenizer	p. 26
Graphics	p. 27
Printing	p. 36
Summary	p. 37
References and Further Reading	p. 37
High Performance Computing	p. 39
Parallel Computing	p. 39
Java Threads	p. 40
An Example of Parallel Computing	p. 41
Distributed Computing	p. 41
Remote Method Invocation	p. 41
An RMI Client	p. 42
The Remote Interface	p. 44
Serialization	p. 48
A Reflective RMI Server	p. 48
Reflection	p. 51
Build and Run the Server	p. 51
Build and Run the Client	p. 53
Summary	p. 54
Appendix	p. 55
References and Further Reading	p. 55
Computing
Simulated Annealing	p. 59
Introduction	p. 59
Metropolis Algorithm	p. 60
Ising Model	p. 61
Cooling Schedule	p. 62
3-Dimensional Plot and Animation	p. 62
An Annealing Example	p. 63
Minimization of Functions of Continuous Variables	p. 77
Summary	p. 78
References and Further Reading	p. 79
Artificial Neural Network	p. 81
Introduction	p. 81
Structural vs. Temporal Pattern Recognition	p. 84
Recurrent Neural Network	p. 84
Steps in Designing a Forecasting Neural Network	p. 86
How Many Hidden Neurons/Layers?	p. 87
Error Function	p. 87
Kohonen Self-Organizing Map	p. 88
Unsupervised Learning	p. 89
A Clustering Example	p. 90
Summary	p. 99
References and Further Reading	p. 100
Genetic Algorithm	p. 101
Evolution	p. 101
Crossover	p. 102
Mutation	p. 103
Selection	p. 104
Traveling Salesman Problem	p. 105
Genetic Programming	p. 113
Prospects	p. 114
Summary	p. 115
References and Further Reading	p. 115
Monte Carlo Simulation	p. 117
Random Number Generators	p. 117
Inverse Transform Method	p. 118
Acceptance-Rejection (Von Neumann) Method	p. 119
Error Estimation	p. 120
Multivariate Distribution with a Specified Correlation Matrix	p. 121
Stochastic-Volatility Jump-Diffusion Process	p. 122
A Cash Flow Example	p. 123
Variance Reduction Techniques	p. 130
Summary	p. 130
References and Further Reading	p. 131
Molecular Dynamics	p. 133
Computer Experiment	p. 133
Statistical Mechanics	p. 134
Ergodicity	p. 135
Lennard-Jones Potential	p. 135
Velocity Verlet Algorithm	p. 137
Correcting for Finite Size and Finite Time	p. 138
An Evaporation Example	p. 139
Summary	p. 144
References and Further Reading	p. 145
Cellular Automata	p. 147
Complexity	p. 147
Self-Organized Criticality	p. 148
Simulation by Cellular Automata	p. 148
Lattice Gas Automata	p. 149
A Hydrodynamic Example	p. 151
Summary	p. 164
References and Further Reading	p. 165
Path Integral	p. 167
Feynman's Sum Over Histories	p. 167
Numerical Path Integration and Feynman-Kac Formula	p. 170
Options in Finance	p. 171
A Path Integral Approach to Option Pricing	p. 171
Importance Sampling (Metropolis-Hastings algorithm)	p. 172
Implementation	p. 174
Summary	p. 179
References and Further Reading	p. 180
Data Fitting	p. 181
Chi-Square	p. 181
Marquardt Recipe	p. 182
Uncertainties in the Best-Fit Parameters	p. 183
Arbitrary Distributions by Monte Carlo	p. 183
A Surface Fit Example	p. 187
Summary	p. 193
References and Further Reading	p. 194
Bayesian Analysis	p. 195
Bayes Theorem	p. 195
Principle of Maximum Entropy	p. 196
Likelihood Function	p. 197
Image/Spectrum Restoration	p. 198
An Iterative Procedure	p. 201
A Pixon Example	p. 202
Summary	p. 209
References and Further Reading	p. 210
Graphical Model	p. 211
Directed Graphs	p. 211
Bayesian Information Criterion	p. 212
Kalman Filter	p. 214
A Progressive Procedure	p. 215
Kalman Smoother	p. 217
Initialization of the Filter	p. 218
Helix Tracking	p. 218
Buffered I/O	p. 221
The Kalman Code	p. 226
H Infinity Filter	p. 235
Properties of H Infinity Filters	p. 236
Summary	p. 237
References and Further Reading	p. 238
JNI Technology	p. 241
Java Native Interface	p. 241
JNI How-To	p. 242
Call Fortran Programs from C	p. 242
A JNI Example	p. 244
Summary	p. 251
References and Further Reading	p. 251
Appendices	p. 253
	p. 253
Web Computing	p. 253
Class Sources	p. 255
Index	p. 261
Table of Contents provided by Ingram. All Rights Reserved.

Interdisciplinary Computing in Java Programming

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Hardcover

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