<Numerical Recipes in C> Code with Examples

This repository contains code from the book <Numerical Recipes in C> with my comments. The code are contained in a function, which is called in the main function with example inputs. All code can be compiled and run without input parameter.

A set of notes for each chapter/topic is also available on my Github.io blog.

Note that the original code from the book use some tricks with pointers so that indices could start from 1 instead of 0 (Why!). Since I prefer 0-indexing for C/C++, all code in this repository have indicies starting from 0. You are welcome!

Usage

For MacOS player, the code can be compiled by clang util.c file_to_compile.c -o programme. The compiled code can be then run by ./programme.

For Windows player, if gcc is installed, the code can be compiled by gcc util.c file_to_compile.c -o programme. The compiled code can then be run by ./programme.exe.

Content

The code included in this repository, in correspondence to the book's chapters:

Chapter 2 Solution of Linear Algebraic Equations

Notes for this chapter are availablbe at http://jadecci.github.io/notes/C-Chpt2.html

• Gauss-Jordan Elimination with full pivoting: chpt2_gauss_jordan_full_pivot.c

• LU Decomposition: chpt2_LU_decomposition.c

• Cholesky Decomposition: chpt2_cholesky_decomposition.c

• To add in the future: Singular Value Decomposition, QR Decomposition

Chapter 3 Interpolation and Extrapolation

• Polynomial

• Cubic Spline

• Higher Dimensionality

Chapter 6 Special Functions

• Gamma, Beta, Factorials, Binomial: chpt6_gamma_bico_beta.c

• Incomplete Gamma, Error, Chi-Square, Cumulative Poisson

• Incomplete Beta, Student's, F, Cumulative Binomial

Chapter 7 Random Numbers

• Uniform Deviates

• Tranformation: Exponential and Normal Deviates

• Rejection: Gamma, Poisson, Binomial Deviates

• Monte Carlo Integration

Chapter 10 Minimisation or Maximisation of Functions

• Golden Section Search (1D)

• Parabolic Interpolation and Brent's Method (1D)

• First Derivatives (1D)

• Downhill Simplex

• Direction Set (Powell's)

• Variable Metric

• Linear Programming and Simplex Method

Chapter 11 Eigensystems

• Jacobi Transforms (symmetric matrix)

• Givens and Householder Reductions

• Eigenvalues and Eigenvectors of tridiagonal matrix

• Hermitian

• Hessenberg Form

• QR Algorithm for Real Hessenberg Matrices

Chapter 14 Statistical Description of Data

Notes for this chapter are available at http://jadecci.github.io/notes/C-Chpt14.html

• Moments (mean, variance, skewness, etc.): chpt14_moments.c

• Distribution comparisons: Student's t-test, F-test, Chi-Square test, Kolmogorov-Smirnov test

• Linear Correlation

• Nonparametric/Rank Correlation

• Comparing 2D Distributions

• Savitzky-Golay Smoothing Filters

Chapter 15 Modeling of Data

• Least Squares (MLE)

• General Linear Least Squares

• Nonlinear models