Java Programs

Java Programs Related to the Book

Book Title: An Introduction to Computational Physics, 2nd Edition
Author: Tao Pang
Publisher: Cambridge University Press
Publication Place: Cambridge, UK
Publication Date: February 2006
ISBN: 0-521-82569-5 (hardback)
List Price: $70
Other Info: 402 Pages; 246 x 189 mm; 37 Line Diagrams; 3 Tables; 169 Exercises; Bibliography; and Index

Please Note:

Most programs listed here have appeared in the book, which are copyrighted along with the book;
No warranties, express or implied, are made for any material at this site.

Chapter 1. Introduction

Program 1.1: One-dimensional motion under a harmonic force.

Chapter 2. Approximation of a function

Program 2.1: Lagrange interpolation with the Aitken method.
Program 2.2: Lagrange interpolation with the upward/downward correction method.
Program 2.3: Orthogonal polynomials generator applied to fit the data of the Millikan experiment.
Program 2.4: A direct linear fit to the data of the Millikan experiment.
Program 2.5: An example of using the cubic spline with the input data file xy.data.
Program 2.6: An example of using the random number generator in throwing darts.
Program 2.7: An example of using the 64-bit random number generator in throwing darts.
Program 2.8: An example of generating exponential random numbers.
Program 2.9: An example of generating Gaussian random numbers.
Program 2.10: An example of generating a two-dimensional percolation network.

Chapter 3. Numerical calculus

Program 3.1: Derivatives with the three-point formula.
Program 3.2: Derivatives with the nonuniform three-point formula.
Program 3.3: Derivatives with the adaptive scheme.
Program 3.4: Integration with the Simpson rule.
Program 3.5: Integration with the adaptive scheme.
Program 3.6: Integration with the nonuniform Simpson rule.
Program 3.7: Root Search with the bisection method.
Program 3.8: Root Search with the Newton method.
Program 3.9: Root Search with the secant method.
Program 3.10: Bond length of NaCl.
Program 3.11: An example of using the steepest-decent method in search of a minimum.
Program 3.12: Classical scattering.

Chapter 4. Ordinary differential equations

Program 4.1: Simplest predictor-corrector scheme.
Program 4.2: Two-point predictor-corrector scheme applied to a motorcycle jump.
Program 4.3: Fourth order Runge-Kutta algorithm applied to the nonlinear pendulum problem.
Program 4.4: Boundary-value problem solved with the shooting method.
Program 4.4: Boundary-value problem in the form of a linear differential equation.
Program 4.5: Simplest algorithm for the Sturm-Liouville equation.
Program 4.6: Eigenvalue problem of the one-dimensional Schroedinger equation.
Program 4.7: Quantum scattering in one dimension.

Chapter 5. Numerical methods for matrices

Program 5.1: Determinant evaluated with the Gaussian elimination scheme.
Program 5.2: Linear equation set solver with the Gaussian elimination scheme.
Program 5.3: Matrix inversion with the Gaussian elimination scheme.
Program 5.4: An application of the multivariable Newton method.
Program 5.5: Generator of the determinant polynomials.
Program 5.6: Matrix inversion with the Faddeev-Leverrier method.
Program 5.7: Evaluation of a complex polynomial.
Program 5.8: Random matrix generator.

Chapter 6. Spectral analysis

Program 6.1: Discrete Fourier transform.
Program 6.2: Fast Fourier transform.
Program 6.3: Fast Fourier transform in two dimensions.
Program 6.4: The continuous wavelet transform.
Program 6.5: The Legendre polynomials generator.
Program 6.6: The Bessel functions generator.

Chapter 7. Partial differential equations

Program 7.1: The bench problem solved with the LU decomposition.
Program 7.2: The bench problem solved with the relaxation scheme.
Program 7.3: Ground water dynamics.
Program 7.4: The time-dependent temperature field.

Chapter 8. Molecular dynamics

Program 8.1: Halley's comet studied with the Verlet algorithm.
Program 8.2: The Maxwell velocity distribution generator.

Chapter 9. Modeling continuous systems

Program 9.1: Solution of one-dimensional Poisson equation with the finite element method.

Chapter 10. Monte Carlo simulations

Program 10.1: An example with random sampling.
Program 10.2: An example with importance sampling.

Chapter 11. Genetic algorithm and programming

Program 11.1: The Thomson problem solved with the discrete variable genetic algorithm.
Program 11.2: The Thomson problem solved with the real variable genetic algorithm.
Program 11.3: The Lennard-Jones clusters optimized with the genetic algorithm.