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INFO-DIR-SECTION Scientific software
START-INFO-DIR-ENTRY
* gsl-ref: (gsl-ref).                   GNU Scientific Library - Reference
END-INFO-DIR-ENTRY

   This file documents the GNU Scientific Library.

   Copyright (C) 1996, 1997, 1998, 1999 The GSL Project.

   Permission is granted to make and distribute verbatim copies of this
manual provided the copyright notice and this permission notice are
preserved on all copies.

   Permission is granted to copy and distribute modified versions of
this manual under the conditions for verbatim copying, provided that
the entire resulting derived work is distributed under the terms of a
permission notice identical to this one.

   Permission is granted to copy and distribute translations of this
manual into another language, under the above conditions for modified
versions, except that this permission notice may be stated in a
translation approved by the Foundation.


File: gsl-ref.info,  Node: Interpolation,  Next: Series Acceleration,  Prev: Roots of Polynomials,  Up: Top

Interpolation
*************

Introduction
============

   This chapter describes functions for performing interpolation.  All
the interpolation methods conform to a model which tries to factor out
the various subtasks for creation, lookup and evaluation.  The state of
an interpolation object is stored in a `gsl_interp_obj'; these are
created by special purpose factories, one for each interpolation
method.  State for searches is stored in a `gsl_interp_accel', which is
a kind of iterator for interpolation lookups.

   The interpolation library provides five interpolation object
factories:

`gsl_interp_factory_linear'
     linear interpolation

`gsl_interp_factory_cspline_natural'
     cubic spline with natural boundary conditions

`gsl_interp_factory_cspline_periodic'
     cubic spline with periodic boundary conditions

`gsl_interp_factory_akima_natural'
     Akima spline with natural boundary conditions

`gsl_interp_factory_akima_periodic'
     Akima spline with periodic boundary conditions

   The interpolation object (`gsl_interp_obj') does not copy the data
arrays but stores all static state computed from the data.  The "x"
data array is always assumed to be strictly ordered; the behaviour for
other arrangements is not defined.

Functions
=========

 - Function: gsl_interp_accel * gsl_interp_accel_new (void)
     Create an accelerator object, which is a kind of iterator for
     interpolation lookups.  It tracks the state of lookups, thus
     allowing for application of various acceleration strategies.

 - Function: size_t gsl_interp_accel_find (gsl_interp_accel * A, const
          double x_array[], size_t SIZE, double X)
     Perform a lookup action on a data array, using the given
     accelerator.  This is how lookups are performed during evaluation
     of an interpolation.  Returns an index such that `xarray[index] <=
     x < xarray[index+1]'.

 - Function: int gsl_interp_eval_impl (const gsl_interp_obj * OBJ,
          const double xa[], const double ya[], double X,
          gsl_interp_accel * A, double * Y)
 - Function: int gsl_interp_eval_e (const gsl_interp_obj * OBJ, const
          double xa[], const double ya[], double X, gsl_interp_accel *
          A, double * Y)
 - Function: double gsl_interp_eval (const gsl_interp_obj * OBJ, const
          double xa[], const double ya[], double X, gsl_interp_accel *
          A)
     Evaluate an interpolation at a given point, doing lookup with the
     given accelerator state.

 - Function: int gsl_interp_eval_deriv_impl (const gsl_interp_obj *
          OBJ, const double xa[], const double ya[], double X,
          gsl_interp_accel * A, double * Y)
 - Function: int gsl_interp_eval_deriv_e (const gsl_interp_obj * OBJ,
          const double xa[], const double ya[], double X,
          gsl_interp_accel * A, double * Y)
 - Function: double gsl_interp_eval_deriv (const gsl_interp_obj * OBJ,
          const double xa[], const double ya[], double X,
          gsl_interp_accel * A)
     Evaluate the derivative of an interpolation at a given point.

 - Function: void gsl_interp_obj_free (gsl_interp_obj * INTERP_OBJ)
     Free an interpolation object.

 - Function: void gsl_interp_accel_free (gsl_interp_accel* A)
     Free an accelerator object.

Examples
========

Creating and Using a Linear Interpolation
-----------------------------------------

   The steps are to obtain a factory reference, create an interpolation
object from given data, create an accelerator object (iterator), use
the interpolation object and accelerator, and finally free resources.

       double x[64];
       double y[64];
       double x0;
       double y0;
       ...
       gsl_interp_factory f = gsl_interp_factory_linear;
       gsl_interp_obj * interp = f.create(x, y, 64);
       gsl_interp_accel * a = gsl_interp_accel_new ();
       ...
       x0 = 5.7;
       gsl_interp_eval_impl(interp, x, y, x0, a, &y0);
       ...
       gsl_interp_accel_free (a);
       gsl_interp_obj_free (interp);

Creating and Using Other Interpolations
---------------------------------------

   To change the above code to use, say cubic splines with periodic
boundary conditions, the only statement that would be changed is the
one which obtains the reference to the factory.  We would do

       ...
       /* gsl_interp_factory f = gsl_interp_factory_linear; */
       gsl_interp_factory f = gsl_interp_factory_cspline_periodic
       ...

   With this design, the interplation method can be changed
dynamically, at runtime if desired, since the main block of code is
unchanging and will work with any valid factory reference.


File: gsl-ref.info,  Node: Series Acceleration,  Next: Random Number Generation,  Prev: Interpolation,  Up: Top

Series Acceleration
*******************

   The functions described in this chapter accelerate the convergence
of a series using the Levin u-transform.  This method takes a small
number of terms from the start of a series and uses a systematic
approximation to compute an extrapolated value and an estimate of its
error.  The u-transform works for both convergent and divergent series,
including asymptotic series.

   These functions are declared in the header file `gsl_sum.h'.

* Menu:

* Acceleration functions::
* Example of accelerating a series::
* Series Acceleration References::


File: gsl-ref.info,  Node: Acceleration functions,  Next: Example of accelerating a series,  Up: Series Acceleration

Acceleration functions
======================

 - Function: int gsl_sum_levin_u_accel (const double * ARRAY, size_t
          ARRAY_SIZE, double * Q_NUM, double * Q_DEN, double * DQ_NUM,
          double * DQ_DEN, double * DSUM, double * SUM_ACCEL, double *
          SUM_PLAIN, double * PRECISION)
     This function takes the terms of a series in ARRAY of size
     ARRAY_SIZE and computes the extrapolated limit of the series using
     a Levin u-transform.  The extrapolated sum is stored in SUM_ACCEL,
     with an estimate of the relative error stored in PRECISION.  The
     actual term-by-term sum is returned in SUM_PLAIN.  Additional
     working space must be provided in the arrays Q_NUM, Q_DEN, DSUM of
     size ARRAY_SIZE elements each and in DQ_NUM, DQ_DEN of size
     ARRAY_SIZE**2.  The algorithm estimates both truncation error and
     round-off error to choose an optimal number of terms for the
     extrapolation.


File: gsl-ref.info,  Node: Example of accelerating a series,  Next: Series Acceleration References,  Prev: Acceleration functions,  Up: Series Acceleration

Example of accelerating a series
================================

   The following code calculates an estimate of \zeta(2) = \pi^2 / 6
using the series,

     \zeta(2) = 1 + 1/2^2 + 1/3^2 + 1/4^2 + ...

After N terms the error in the sum is O(1/N), making direct summation
of the series converge slowly.

     #include <stdio.h>
     #include <gsl/gsl_math.h>
     #include <gsl/gsl_sum.h>
     
     #define N 20
     
     int
     main (void)
     {
       double t[N], qnum[N], qden[N], dsum[N], dqnum[N * N], dqden[N * N];
       double sum_accel, sum_plain, prec;
       double sum = 0;
       size_t n_used ;
       int n;
     
       const double zeta_2 = M_PI * M_PI / 6.0;
     
       /* terms for zeta(2) = \sum_{n=0}^{\infty} 1/n^2 */
     
       for (n = 0; n < N; n++)
         {
           double np1 = n + 1.0;
           t[n] = 1.0 / (np1 * np1);
           sum += t[n] ;
         }
     
       gsl_sum_levin_u_accel (t, N, qnum, qden, dqnum, dqden, dsum,
                              &sum_accel, &n_used, &sum_plain, &prec);
     
       printf("term-by-term sum = % .16f using %d terms\n", sum, N) ;
       printf("term-by-term sum = % .16f using %d terms\n", sum_plain, n_used) ;
       printf("exact value      = % .16f\n", zeta_2) ;
       printf("accelerated sum  = % .16f using %d terms\n", sum_accel, n_used) ;
       printf("estimated error  = % .16f\n", prec * sum_accel) ;
       printf("actual error     = % .16f\n", sum_accel - zeta_2) ;
     
       return 0;
     }

The ouput below shows that the Levin u-transform is able to obtain an
estimate of the sum to 1 part in 10^10 using the first eleven terms of
the series.  The error estimate returned by the function is also
accurate, giving the correct number of significant digits.

     bash$ ./a.out
     term-by-term sum =  1.5961632439130233 using 20 terms
     term-by-term sum =  1.5759958390005426 using 13 terms
     exact value      =  1.6449340668482264
     accelerated sum  =  1.6449340668166479 using 13 terms
     estimated error  =  0.0000000000508580
     actual error     = -0.0000000000315785

Note that a direct summation of this series would require 10^10 terms
to achieve the same precision as the accelerated sum does in 13 terms.


File: gsl-ref.info,  Node: Series Acceleration References,  Prev: Example of accelerating a series,  Up: Series Acceleration

References and Further Reading
==============================

The algorithms used by these functions are described in the following
papers,

     T. Fessler, W.F. Ford, D.A. Smith, HURRY: An acceleration
     algorithm for scalar sequences and series `ACM Transactions on
     Mathematical Software', 9(3):346-354, 1983.  and Algorithm 602
     9(3):355-357, 1983.

The theory of the u-transform was presented by Levin,

     D. Levin, Development of Non-Linear Transformations for Improving
     Convergence of Sequences, `Intern. J. Computer Math.' B3:371-388,
     1973


File: gsl-ref.info,  Node: Random Number Generation,  Next: Random Number Distributions,  Prev: Series Acceleration,  Up: Top

Random Number Generation
************************

   The GNU Scientific Library provides a large collection of random
number generators which can be accessed through a uniform interface.
Environment variables allow you to select different generators and
seeds at runtime, so that you can easily switch between generators
without needing to recompile your program.  Each instance of a
generator keeps track of its own state, allowing the generators to be
used in multi-threaded programs.  Additional functions are available
for transforming uniform random numbers into samples from continuous or
discrete probability distributions such as the Gaussian, log-normal or
Poisson distributions.

* Menu:

* General comments on random numbers::
* The Random Number Generator Interface::
* Random number generator initialization::
* Sampling from a random number generator::
* Auxiliary random number generator functions::
* Random number environment variables::
* Saving and restoring random number generator state::
* Random number generator algorithms::
* Unix random number generators::
* Numerical Recipes generators::
* Other random number generators::
* Random Number Generator Performance::
* Random Number References and Further Reading::
* Random Number Acknowledgements::


File: gsl-ref.info,  Node: General comments on random numbers,  Next: The Random Number Generator Interface,  Up: Random Number Generation

General comments on random numbers
==================================

   In 1988, Park and Miller wrote a paper entitled "Random number
generators: good ones are hard to find." [Commun. ACM, 31, 1192-1201].
Fortunately, some excellent random number generators are available,
though poor ones are still in common use.  You may be happy with the
system-supplied random number generator on your computer, but you should
be aware that as computers get faster, requirements on random number
generators increase.  Nowadays, a simulation that calls a random number
generator millions of times can often finish before you can make it down
the hall to the coffee machine and back.

   A very nice review of random number generators was written by Pierre
L'Ecuyer, as Chapter 4 of the book: Handbook on Simulation, Jerry Banks,
ed. (Wiley, 1997).  The chapter is available in postscript from from
L'Ecuyer's ftp site (see references).  Knuth's volume on Seminumerical
Algorithms (originally published in 1968) devotes 170 pages to random
number generators, and has recently been updated in its 3rd edition
(1997).  It is brilliant, a classic.  If you don't own it, you should
stop reading right now, run to the nearest bookstore, and buy it.

   A good random number generator will satisfy both theoretical and
statistical properties.  Theoretical properties are often hard to obtain
(they require real math!), but one prefers a random number generator
with a long period, low serial correlation, and a tendency _not_ to
"fall mainly on the planes."  Statistical tests are performed with
numerical simulations.  Generally, a random number generator is used to
estimate some quantity for which the theory of probability provides an
exact answer.  Comparison to this exact answer provides a measure of
"randomness".


File: gsl-ref.info,  Node: The Random Number Generator Interface,  Next: Random number generator initialization,  Prev: General comments on random numbers,  Up: Random Number Generation

The Random Number Generator Interface
=====================================

   It is important to remember that a random number generator is not a
"real" function like sine or cosine.  Unlike real functions, successive
calls to a random number generator yield different return values.  Of
course that is just what you want for a random number generator, but to
achieve this effect, the generator must keep track of some kind of
"state" variable.  Sometimes this state is just an integer (sometimes
just the value of the previously generated random number), but often it
is more complicated than that and may involve a whole array of numbers,
possibly with some indices thrown in.  To use the random number
generators, you do not need to know the details of what comprises the
state, and besides that varies from algorithm to algorithm.

   The random number generator library uses two special structs,
`gsl_rng_type' which holds static information about each type of
generator and `gsl_rng' which describes an instance of a generator
created from a given `gsl_rng_type'.

   The functions described in this section are declared in the header
file `gsl_rng.h'.


File: gsl-ref.info,  Node: Random number generator initialization,  Next: Sampling from a random number generator,  Prev: The Random Number Generator Interface,  Up: Random Number Generation

Random number generator initialization
======================================

 - Random: gsl_rng * gsl_rng_alloc (gsl_rng_type * T)
     This function returns a pointer to a newly-created instance of a
     random number generator of type T.  For example, the following
     code creates an instance of the Tausworthe generator,

          gsl_rng * r = gsl_rng_alloc (gsl_rng_taus);

     If there is insufficient memory to create the generator then the
     function returns a null pointer and the error handler is invoked
     with an error code of `GSL_ENOMEM'.

     The generator is automatically initialized with the default seed,
     `gsl_rng_default_seed'.  This is zero by default but can be changed
     either directly or by using the environment variable
     `GSL_RNG_SEED', *note Random number environment variables::..

     Some of the defined generator types are,

          gsl_rng_cmrg, gsl_rng_minstd, gsl_rng_mrg, gsl_rng_mt19937,
          gsl_rng_r250, gsl_rng_ran0, gsl_rng_ran1, gsl_rng_ran2,
          gsl_rng_ran3, gsl_rng_rand, gsl_rng_rand48,
          gsl_rng_random_bsd, gsl_rng_random_glibc2,
          gsl_rng_random_libc5, gsl_rng_randu, gsl_rng_ranf,
          gsl_rng_ranlux, gsl_rng_ranlux389, gsl_rng_ranmar,
          gsl_rng_slatec, gsl_rng_taus, gsl_rng_tds, gsl_rng_tt800,
          gsl_rng_uni, gsl_rng_uni32, gsl_rng_vax, gsl_rng_zuf
     The details of each generator are given later in this chapter.

 - Random: void gsl_rng_set (const gsl_rng * R, unsigned long int S)
     This function initializes (or `seeds') the random number
     generator.  If the generator is seeded with the same value of S on
     two different runs, the same stream of random numbers will be
     generated by successive calls to the routines below.  If different
     values of S are supplied, then the generated streams of random
     numbers should be completely different.  If the seed S is zero
     then the standard seed from the original implementation is used
     instead.  For example, the original Fortran source code for the
     `ranlux' generator used a seed of 314159265, and so choosing S
     equal to zero reproduces this when using `gsl_rng_ranlux'.

 - Random: void gsl_rng_free (gsl_rng * R)
     This function frees all the memory associated with the generator R.


File: gsl-ref.info,  Node: Sampling from a random number generator,  Next: Auxiliary random number generator functions,  Prev: Random number generator initialization,  Up: Random Number Generation

Sampling from a random number generator
=======================================

   The following functions return uniformly distributed random numbers,
either as integers or double precision floating point numbers.  To
obtain non-uniform distributions *note Random Number Distributions::..

 - Random: unsigned long int gsl_rng_get (const gsl_rng * R)
     This function returns a random integer from the generator R.  The
     minimum and maximum values depend on the algorithm used, but all
     integers in the range [MIN,MAX] are equally likely.  The values of
     MIN and MAX can determined using the auxiliary functions
     `gsl_rng_max (r)' and `gsl_rng_min (r)'.

 - Random: double gsl_rng_uniform (const gsl_rng * R)
     This function returns a double precision floating point number
     uniformly distributed in the range [0,1).  The range includes 0.0
     but excludes 1.0.  The value is typically obtained by dividing the
     result of `gsl_rng_get(r)' by `gsl_rng_max(r) + 1.0' in double
     precision.  Some generators compute this ratio internally so that
     they can provide floating point numbers with more than 32 bits of
     randomness (the maximum number of bits that can be portably
     represented in a single `unsigned long int').

 - Random: double gsl_rng_uniform_pos (const gsl_rng * R)
     This function returns a positive double precision floating point
     number uniformly distributed in the range (0,1), excluding both
     0.0 and 1.0.  The number is obtained by sampling the generator
     with the algorithm of `gsl_rng_uniform' until a non-zero value is
     obtained.  You can use this function if you need to avoid a
     singularity at 0.0.

 - Random: unsigned long int gsl_rng_uniform_int (const gsl_rng * R,
          unsigned long int N)
     This function returns a random integer from 0 to N-1 inclusive.
     All integers in the range [0,N-1] are equally likely, regardless
     of the generator used.  An offset correction is applied so that
     zero is always returned with the correct probability, for any
     minimum value of the underlying generator.

     If N is larger than the range of the generator then the function
     calls the error handler with an error code of `GSL_EINVAL' and
     returns zero.


File: gsl-ref.info,  Node: Auxiliary random number generator functions,  Next: Random number environment variables,  Prev: Sampling from a random number generator,  Up: Random Number Generation

Auxiliary random number generator functions
===========================================

   The following functions provide information about an existing
generator.  You should use them in preference to hard-coding the
generator parameters into your own code.

 - Random: const char * gsl_rng_name (const gsl_rng * R)
     This function returns a pointer to the name of the generator.  For
     example,

          printf("r is a '%s' generator\n", gsl_rng_name (r)) ;

     would print something like `r is a 'taus' generator'

 - Random: unsigned long int gsl_rng_max (const gsl_rng * R)
     `gsl_rng_max' returns the largest value that `gsl_rng_get' can
     return.

 - Random: unsigned long int gsl_rng_min (const gsl_rng * R)
     `gsl_rng_min' returns the smallest value that `gsl_rng_get' can
     return.  Usually this value is zero.  There are some generators
     with algorithms that cannot return zero, and for these generators
     the minimum value is 1.

 - Random: void * gsl_rng_state (const gsl_rng * R)
 - Random: size_t gsl_rng_size (const gsl_rng * R)
     These function return a pointer to the state of generator R and
     its size.  You can use this information to access the state
     directly.  For example, the following code will write the state of
     a generator to a stream,

          void * state = gsl_rng_state (r);
          size_t n = gsl_rng_size (r);
          fwrite (state, n, 1, stream);



File: gsl-ref.info,  Node: Random number environment variables,  Next: Saving and restoring random number generator state,  Prev: Auxiliary random number generator functions,  Up: Random Number Generation

Random number environment variables
===================================

   The library allows you to choose a default generator and seed from
the environment variables `GSL_RNG_TYPE' and `GSL_RNG_SEED' and the
function `gsl_rng_env_setup'.  This makes it easy try out different
generators and seeds without having to recompile your program.

 - Function: const gsl_rng_type * gsl_rng_env_setup (void)
     This function reads the environment variables `GSL_RNG_TYPE' and
     `GSL_RNG_SEED' and uses their values to set the corresponding
     library variables `gsl_rng_default' and `gsl_rng_default_seed'.
     These global variables are defined as follows,

          extern const gsl_rng_type *gsl_rng_default
          extern unsigned long int gsl_rng_default_seed

     The environment variable `GSL_RNG_TYPE' should be the name of a
     generator, such as `taus' or `mt19937'.  The environment variable
     `GSL_RNG_SEED' should contain the desired seed value.  It is
     converted to an `unsigned long int' using the C library function
     `strtoul'.

     If you don't specify a generator for `GSL_RNG_TYPE' then
     `gsl_rng_mt19937' is used as the default.  The initial value of
     `gsl_rng_default_seed' is zero.


Here is a short program which shows how to create a global generator
using the environment variables `GSL_RNG_TYPE' and `GSL_RNG_SEED',

     #include <stdio.h>
     #include <gsl/gsl_rng.h>
     
     gsl_rng * r ;  /* global generator */
     
     int
     main ()
     {
       gsl_rng_env_setup() ;
     
       r = gsl_rng_alloc (gsl_rng_default);
     
       printf("generator type: %s\n", gsl_rng_name (r));
       printf("seed = %u\n", gsl_rng_default_seed);
       printf("first value = %u\n", gsl_rng_get (r)) ;
     }

Running the program without any environment variables uses the initial
defaults, an `mt19937' generator with a seed of 0,

     bash$ ./a.out
     generator type: mt19937
     seed = 0
     first value = 3510405877

By setting the two variables on the command line we can change the
default generator and the seed,

     bash$ GSL_RNG_TYPE="taus" GSL_RNG_SEED=123 ./a.out
     GSL_RNG_TYPE=taus
     GSL_RNG_SEED=123
     generator type: taus
     seed = 123
     first value = 2720986350


File: gsl-ref.info,  Node: Saving and restoring random number generator state,  Next: Random number generator algorithms,  Prev: Random number environment variables,  Up: Random Number Generation

Saving and restoring random number generator state
==================================================

   The above methods ignore the random number `state' which changes from
call to call.  It is often useful to be able to save and restore the
state.  To permit these practices, a few somewhat more advanced
functions are supplied.  These include:

 - Random: int gsl_rng_memcpy (gsl_rng * DEST, const gsl_rng * SRC)
     This function copies the random number generator SRC into the
     pre-existing generator DEST, making DEST into an exact copy of
     SRC.  The two generators must be of the same type.

 - Random: gsl_rng * gsl_rng_clone (const gsl_rng * R)
     This function returns a pointer to a newly created generator which
     is an exact copy of the generator R.

 - Random: void gsl_rng_print_state (const gsl_rng * R)
     This function prints a hex-dump of the state of the generator R to
     `stdout'.  At the moment its only use is for debugging.


File: gsl-ref.info,  Node: Random number generator algorithms,  Next: Unix random number generators,  Prev: Saving and restoring random number generator state,  Up: Random Number Generation

Random number generator algorithms
==================================

   The functions described above make no reference to the actual
algorithm used.  This is deliberate so that you can switch algorithms
without having to change any of your application source code.  The
library provides a large number of generators of different types,
including simulation quality generators, generators provided for
compatibility with other libraries and historical generators from the
past.

   The following generators are recommended for use in simulation.  They
have extremely long periods, low correlation and pass most statistical
tests.

 - Generator: gsl_rng_mt19937
     The MT19937 generator of Makoto Matsumoto and Takuji Nishimura is a
     variant of the twisted generalized feedback shift-register
     algorithm, and is known as the "Mersenne Twister" generator.  It
     has a Mersenne prime period of 2^19937 - 1 (about 10^6000) and is
     equi-distributed in 623 dimensions.  It has passed the DIEHARD
     statistical tests.  It uses 624 words of state per generator and is
     comparable in speed to the other generators.  The original
     generator used a default seed of 4357 and choosing S equal to zero
     in `gsl_rng_set' reproduces this.

     For more information see,
          Makoto Matsumoto and Takuji Nishimura, "Mersenne Twister: A
          623-dimensionally equidistributed uniform pseudorandom number
          generator". `ACM Transactions on Modeling and Computer
          Simulation', Vol. 8, No. 1 (Jan. 1998), Pages 3-30

 - Generator: gsl_rng_ranlxs0
 - Generator: gsl_rng_ranlxs1
 - Generator: gsl_rng_ranlxs2
     The generator `ranlxs0' is a second-generation version of the
     RANLUX algorithm of Lu"scher, which produces "luxury random
     numbers".  This generator provides single precision output (24
     bits) at three luxury levels `ranlxs0', `ranlxs1' and `ranlxs2'.
     It uses double-precision floating point arithmetic internally and
     can be significantly faster than the integer version of `ranlux',
     particularly on 64-bit architectures.  The period of the generator
     is about 10^171.  The algorithm has mathematically proven
     properties and can provide truly decorrelated numbers at a known
     level of randomness.

 - Generator: gsl_rng_ranlxd1
 - Generator: gsl_rng_ranlxd2
     These generators produce double precision output (48 bits) from the
     RANLXS generator.  The library provides two luxury levels
     `ranlxd1' and `ranlxd2'.

 - Generator: gsl_rng_ranlux
 - Generator: gsl_rng_ranlux389
     The `ranlux' generator is an implemenation of the original
     algorithm developed by Lu"scher.  It uses a
     lagged-fibonacci-with-skipping algorithm to produce "luxury random
     numbers".  It is a 24-bit generator, originally designed for
     single-precision IEEE floating point numbers.  This implementation
     is based on integer arithmetic, while the second-generation
     versions RANLXS and RANLXD described above provide floating-point
     implementations which will be faster on many platforms.  The
     period of the generator is about 10^171.  The algorithm has
     mathematically proven properties and it can provide truly
     decorrelated numbers at a known level of randomness.  The default
     level of decorrelation recommended by Lu"scher is provided by
     `gsl_rng_ranlux', while `gsl_rng_ranlux389' gives the highest
     level of randomness, with all 24 bits decorrelated.  Both types of
     generator use 24 words of state per generator.

     For more information see,
          M. Lu"scher, "A portable high-quality random number generator
          for lattice field theory calculations", `Computer Physics
          Communications', 79 (1994) 100-110.

          F. James, "RANLUX: A Fortran implementation of the
          high-quality pseudo-random number generator of Lu"scher",
          `Computer Physics Communications', 79 (1994) 111-114

 - Generator: gsl_rng_cmrg
     This is a combined multiple recursive generator by L'Ecuyer.  Its
     sequence is,

          z_n = (x_n - y_n) mod m_1

     where the two underlying generators x_n and y_n are,

          x_n = (a_1 x_{n-1} + a_2 x_{n-2} + a_3 x_{n-3}) mod m_1
          y_n = (b_1 y_{n-1} + b_2 y_{n-2} + b_3 y_{n-3}) mod m_2

     with coefficients a_1 = 0, a_2 = 63308, a_3 = -183326, b_1 = 86098,
     b_2 = 0, b_3 = -539608, and moduli m_1 = 2^31 - 1 = 2147483647 and
     m_2 = 2145483479.

     The period of this generator is 2^205 (about 10^61).  It uses 6
     words of state per generator.  For more information see,

          P. L'Ecuyer, "Combined Multiple Recursive Random Number
          Generators," `Operations Research', 44, 5 (1996), 816-822.

 - Generator: gsl_rng_mrg
     This is a fifth-order multiple recursive generator by L'Ecuyer,
     Blouin and Coutre.  Its sequence is,

          x_n = (a_1 x_{n-1} + a_5 x_{n-5}) mod m

     with a_1 = 107374182, a_2 = a_3 = a_4 = 0, a_5 = 104480 and m =
     2^31 - 1.

     The period of this generator is about 10^46.  It uses 5 words of
     state per generator.  More information can be found in the
     following paper,
          P. L'Ecuyer, F. Blouin, and R. Coutre, "A search for good
          multiple recursive random number generators", `ACM
          Transactions on Modeling and Computer Simulation' 3, 87-98
          (1993).

 - Generator: gsl_rng_taus
     This is a maximally equidistributed combined Tausworthe generator
     by L'Ecuyer.  The sequence is,

          x_n = (s1_n ^^ s2_n ^^ s3_n)

     where,

          s1_{n+1} = (((s1_n&4294967294)<<12)^^(((s1_n<<13)^^s1_n)>>19))
          s2_{n+1} = (((s2_n&4294967288)<< 4)^^(((s2_n<< 2)^^s2_n)>>25))
          s3_{n+1} = (((s3_n&4294967280)<<17)^^(((s3_n<< 3)^^s3_n)>>11))

     computed modulo 2^32.  In the formulas above ^^ denotes
     "exclusive-or".  Note that the algorithm relies on the properties
     of 32-bit unsigned integers and has been implemented using a
     bitmask of `0xFFFFFFFF' to make it work on 64 bit machines.

     The period of this generator is 2^88 (about 10^26).  It uses 3
     words of state per generator.  For more information see,

          P. L'Ecuyer, "Maximally Equidistributed Combined Tausworthe
          Generators", `Mathematics of Computation', 65, 213 (1996),
          203-213.

 - Generator: gsl_rng_gfsr4
     The `gfsr4' generator is like a lagged-fibonacci generator, and
     produces each number as an `xor''d sum of four previous values.

          r_n = r_{n-A} ^^ r_{n-B} ^^ r_{n-C} ^^ r_{n-D}

     Ziff (ref below) notes that "it is now widely known" that two-tap
     registers (such as R250, which is described below) have serious
     flaws, the most obvious one being the three-point correlation that
     comes from the defn of the generator.  Nice mathematical
     properties can be derived for GFSR's, and numerics bears out the
     claim that 4-tap GFSR's with appropriately chosen offsets are as
     random as can be measured, using the author's test.

     This implementation uses the values suggested the the example on
     p392 of Ziff's article: A=471, B=1586, C=6988, D=9689.

     If the offsets are appropriately chosen (such the one ones in this
     implementation), then the sequence is said to be maximal.  I'm not
     sure what that means, but I would guess that means all states are
     part of the same cycle, which would mean that the period for this
     generator is astronomical; it is (2^K)^D \approx 10^{93334} where
     K=32 is the number of bits in the word, and D is the longest lag.
     This would also mean that any one random number could easily be
     zero; ie 0 <= r < 2^32.

     Ziff doesn't say so, but it seems to me that the bits are
     completely independent here, so one could use this as an efficient
     bit generator; each number supplying 32 random bits.

     For more information see,
          Robert M. Ziff, "Four-tap shift-register-sequence
          random-number generators", `Computers in Physics', 12(4),
          Jul/Aug 1998, pp 385-392.


File: gsl-ref.info,  Node: Unix random number generators,  Next: Numerical Recipes generators,  Prev: Random number generator algorithms,  Up: Random Number Generation

Unix random number generators
=============================

   The standard Unix random number generators `rand', `random' and
`rand48' are provided as part of GSL. Although these generators are
widely available individually often they aren't all available on the
same platform.  This makes it difficult to write portable code using
them and so we have included the complete set of Unix generators in GSL
for convenience.  Note that these generators don't produce high-quality
randomness and aren't suitable for work requiring accurate statistics.
However, if you won't be measuring statistical quantities and just want
to introduce some variation into your program then these generators are
quite acceptable.

 - Generator: gsl_rng_rand
     This is the BSD `rand()' generator.  Its sequence is

          x_{n+1} = (a x_n + c) mod m

     with a = 1103515245, c = 12345 and m = 2^31.  The seed specifies
     the initial value, x_1.  The period of this generator is 2^31, and
     it uses 1 word of storage per generator.

 - Generator: gsl_rng_random_bsd
 - Generator: gsl_rng_random_libc5
 - Generator: gsl_rng_random_glibc2
     These generators implement the `random()' family of functions, a
     set of linear feedback shift register generators originally used
     in BSD Unix.  There are several versions of `random()' in use
     today: the original BSD version (e.g. on SunOS4), a libc5 version
     (common on existing GNU/Linux systems) and a glibc2 version.  Each
     version uses a different seeding procedure, and thus produces
     different sequences.

     The original BSD routines accepted a variable length buffer for the
     generator state, with longer buffers providing higher-quality
     randomness.  The `random()' function implemented algorithms for
     buffer lengths of 8, 32, 64, 128 and 256 bytes, and the algorithm
     with the largest length that would fit into the user-supplied
     buffer was used.  To support these algorithms additional
     generators are available with the following names,

          gsl_rng_random8_bsd
          gsl_rng_random32_bsd
          gsl_rng_random64_bsd
          gsl_rng_random128_bsd
          gsl_rng_random256_bsd

     where the numeric suffix indicates the buffer length.  The
     original BSD `random' function used a 128-byte default buffer and
     so `gsl_rng_random_bsd' has been made equivalent to
     `gsl_rng_random128_bsd'.  Corresponding versions of the `libc5'
     and `glibc2' generators are also available, with the names
     `gsl_rng_random8_libc5', `gsl_rng_random8_glibc2', etc.

 - Generator: gsl_rng_rand48
     This is the Unix `rand48' generator.  Its sequence is

          x_{n+1} = (a x_n + c) mod m

     defined on 48-bit unsigned integers with a = 25214903917, c = 11
     and m = 2^48.  The seed specifies the upper 32 bits of the initial
     value, x_1, with the lower 16 bits set to `0x330E'.  The function
     `gsl_rng_get' returns the upper 32 bits from each term of the
     sequence.  This does not have a direct parallel in the original
     `rand48' functions, but forcing the result to type `long int'
     reproduces the output of `mrand48'.  The function
     `gsl_rng_uniform' uses the full 48 bits of internal state to return
     the double precision number x_n/m, which is equivalent to the
     function `drand48'.  Note that some versions of the GNU C Library
     contained a bug in `mrand48' function which caused it to produce
     different results (only the lower 16-bits of the return value were
     set).


File: gsl-ref.info,  Node: Numerical Recipes generators,  Next: Other random number generators,  Prev: Unix random number generators,  Up: Random Number Generation

Numerical Recipes generators
============================

   The following generators are provided for compatibility with
`Numerical Recipes'.  Note that the original Numerical Recipes
functions used single precision while we use double precision.  This
will lead to minor discrepancies, but only at the level of
single-precision rounding error.  If necessary you can force the
returned values to single precision by storing them in a `volatile
float', which prevents the value being held in a register with double
or extended precision.  Apart from this difference the underlying
algorithms for the integer part of the generators are the same.

 - Generator: gsl_rng_ran0
     Numerical recipes `ran0' implements Park and Miller's MINSTD
     algorithm with a modified seeding procedure.

 - Generator: gsl_rng_ran1
     Numerical recipes `ran1' implements Park and Miller's MINSTD
     algorithm with a 32-element Bayes-Durham shuffle box.

 - Generator: gsl_rng_ran2
     Numerical recipes `ran2' implements a L'Ecuyer combined recursive
     generator with a 32-element Bayes-Durham shuffle-box.

 - Generator: gsl_rng_ran3
     Numerical recipes `ran3' implements Knuth's portable subtractive
     generator.


File: gsl-ref.info,  Node: Other random number generators,  Next: Random Number Generator Performance,  Prev: Numerical Recipes generators,  Up: Random Number Generation

Other random number generators
==============================

   The generators in this section are provided for compatibility with
existing libraries.  If you are converting an existing program to use
GSL then you can select these generators to check your new
implementation against the original one, using the same random number
generator.  After verifying that your new program reproduces the
original results you can then switch to a higher-quality generator.

   Note that most of the generators in this section are based on single
linear congruence relations, which are the least sophisticated type of
generator.  In particular, linear congruences have poor properties when
used with a non-prime modulus, as several of these routines do (e.g.
with a power of two modulus, 2^31 or 2^32).  This leads to periodicity
in the least significant bits of each number, with only the higher bits
having any randomness.  Thus if you want to produce a random bitstream
it is best to avoid using the least significant bits.

   The following generator is provided for compatibility with the CRAY
MATHLIB routine RANF. It produces double precision floating point
numbers which should be identical to those from the original RANF.

 - Generator: gsl_rng_ranf
     This is the CRAY random number generator `RANF'.  Its sequence is

          x_{n+1} = (a x_n) mod m

     defined on 48-bit unsigned integers with a = 44485709377909 and m
     = 2^48.  The seed specifies the lower 32 bits of the initial value,
     x_1, with the lowest bit set to prevent the seed taking an even
     value.  The upper 16 bits of x_1 are set to 0. A consequence of
     this procedure is that the pairs of seeds 2 and 3, 4 and 5, etc
     produce the same sequences.

     There is a subtlety in the implementation of the seeding.  The
     initial state is reversed through one step, by multiplying by the
     modular inverse of a mod m.  This is done for compatibility with
     the original CRAY implementation.

     Note that you can only seed the generator with integers up to
     2^32, while the original CRAY implementation uses non-portable
     wide integers which can cover all 2^48 states of the generator.

     The function `gsl_rng_get' returns the upper 32 bits from each term
     of the sequence.  The function `gsl_rng_uniform' uses the full 48
     bits to return the double precision number x_n/m.

     The period of this generator is 2^46.

 - Generator: gsl_rng_ranmar
     This is the RANMAR lagged-fibonacci generator of Marsaglia, Zaman
     and Tsang.  It is a 24-bit generator, originally designed for
     single-precision IEEE floating point numbers.  It was included in
     the CERNLIB high-energy physics library.

 - Generator: gsl_rng_r250
     This is the shift-register generator of Kirkpatrick and Stoll.  The
     sequence is

          x_n = x_{n-103} ^^ x_{n-250}

     where ^^ denote "exclusive-or", defined on 32-bit words.  The
     period of this generator is about 2^250 and it uses 250 words of
     state per generator.

     For more information see,
          S. Kirkpatrick and E. Stoll, "A very fast shift-register
          sequence random number generator", `Journal of Computational
          Physics', 40, 517-526 (1981)

 - Generator: gsl_rng_tt800
     This is an earlier version of the twisted generalized feedback
     shift-register generator, and has been superseded by the
     development of MT19937.  However, it is still an acceptable
     generator in its own right.  It has a period of 2^800 and uses 33
     words of storage per generator.

     For more information see,
          Makoto Matsumoto and Yoshiharu Kurita, "Twisted GFSR
          Generators II", `ACM Transactions on Modelling and Computer
          Simulation', Vol. 4, No. 3, 1994, pages 254-266.

 - Generator: gsl_rng_vax
     This is the VAX generator `MTH$RANDOM'.  Its sequence is,

          x_{n+1} = (a x_n + c) mod m

     with a = 69069, c = 1 and m = 2^32.  The seed specifies the
     initial value, x_1.  The period of this generator is 2^32 and it
     uses 1 word of storage per generator.

 - Generator: gsl_rng_transputer
     This is the random number generator from the INMOS Transputer
     Development system.  Its sequence is,

          x_{n+1} = (a x_n) mod m

     with a = 1664525 and m = 2^32.  The seed specifies the initial
     value, x_1.

 - Generator: gsl_rng_randu
     This is the IBM `RANDU' generator.  Its sequence is

          x_{n+1} = (a x_n) mod m

     with a = 65539 and m = 2^31.  The seed specifies the initial value,
     x_1.  The period of this generator was only 2^29.  It has become a
     textbook example of a poor generator.

 - Generator: gsl_rng_minstd
     This is Park and Miller's "minimal standard" MINSTD generator, a
     simple linear congruence which takes care to avoid the major
     pitfalls of such algorithms.  Its sequence is,

          x_{n+1} = (a x_n) mod m

     with a = 16807 and m = 2^31 - 1 = 2147483647.  The seed specifies
     the initial value, x_1.  The period of this generator is about
     2^31.

     This generator is used in the IMSL Library (subroutine RNUN) and in
     MATLAB (the RAND function).  It is also sometimes known by the
     acronym "GGL" (I'm not sure what that stands for).

     For more information see,
          Park and Miller, "Random Number Generators: Good ones are
          hard to find", `Communications of the ACM', October 1988,
          Volume 31, No 10, pages 1192-1201.

 - Generator: gsl_rng_uni
 - Generator: gsl_rng_uni32
     This is a reimplementation of the 16-bit SLATEC random number
     generator RUNIF. A generalisation of the generator to 32 bits is
     provided by `gsl_rng_uni32'.  The original source code is
     available from NETLIB.

 - Generator: gsl_rng_slatec
     This is the SLATEC random number generator RAND. It is ancient.
     The original source code is available from NETLIB.

 - Generator: gsl_rng_zuf
     This is the ZUFALL lagged Fibonacci series generator of Peterson.
     Its sequence is,

          t = u_{n-273} + u_{n-607}
          u_n  = t - floor(t)

     The original source code is available from NETLIB.  For more
     information see,
          W. Petersen, "Lagged Fibonacci Random Number Generators for
          the NEC SX-3", `International Journal of High Speed
          Computing' (1994).


File: gsl-ref.info,  Node: Random Number Generator Performance,  Next: Random Number References and Further Reading,  Prev: Other random number generators,  Up: Random Number Generation

Random Number Generator Performance
===================================

   The following table shows the relative performance of a selection the
available random number generators.  The simulation quality generators
which offer the best performance are `taus', `gfsr4' and `mt19937'.

       1754 k ints/sec,    870 k doubles/sec, taus
       1613 k ints/sec,    855 k doubles/sec, gfsr4
       1370 k ints/sec,    769 k doubles/sec, mt19937
        565 k ints/sec,    571 k doubles/sec, ranlxs0
        400 k ints/sec,    405 k doubles/sec, ranlxs1
        490 k ints/sec,    389 k doubles/sec, mrg
        407 k ints/sec,    297 k doubles/sec, ranlux
        243 k ints/sec,    254 k doubles/sec, ranlxd1
        251 k ints/sec,    253 k doubles/sec, ranlxs2
        238 k ints/sec,    215 k doubles/sec, cmrg
        247 k ints/sec,    198 k doubles/sec, ranlux389
        141 k ints/sec,    140 k doubles/sec, ranlxd2
     
       1852 k ints/sec,    935 k doubles/sec, ran3
        813 k ints/sec,    575 k doubles/sec, ran0
        787 k ints/sec,    476 k doubles/sec, ran1
        379 k ints/sec,    292 k doubles/sec, ran2


File: gsl-ref.info,  Node: Random Number References and Further Reading,  Next: Random Number Acknowledgements,  Prev: Random Number Generator Performance,  Up: Random Number Generation

References and Further Reading
==============================

   The subject of random number generation and testing is reviewed
extensively in Knuth's `Seminumerical Algorithms'.

     Donald E. Knuth, `The Art of Computer Programming: Seminumerical
     Algorithms' (Vol 2, 3rd Ed, 1997), Addison-Wesley, ISBN 0201896842.

Further information is available in the review paper written by Pierre
L'Ecuyer, available at
<http://www.iro.umontreal.ca/~lecuyer/myftp/papers/handsim.ps>.

   On the World Wide Web, see the pLab home page
(<http://random.mat.sbg.ac.at/>) for a lot of information on the
state-of-the-art in random number generation, and for numerous links to
various "random" WWW sites.

   The source code for the DIEHARD random number generator tests is also
available online.

     `DIEHARD source code' G. Marsaglia
     <http://stat.fsu.edu/pub/diehard/>


File: gsl-ref.info,  Node: Random Number Acknowledgements,  Prev: Random Number References and Further Reading,  Up: Random Number Generation

Acknowledgements
================

Thanks to Makoto Matsumoto, Takuji Nishimura and Yoshiharu Kurita for
making the source code to their generators (MT19937, MM&TN; TT800,
MM&YK) available under the GNU General Public License.  Thanks to Martin
Lu"scher for providing notes and source code for the RANLXS and RANLXD
generators.


File: gsl-ref.info,  Node: Random Number Distributions,  Next: Permutations,  Prev: Random Number Generation,  Up: Top

Random Number Distributions
***************************

   Distributions of random numbers can be obtained from any of the
generators using the functions described in this section.  In the
simplest cases a non-uniform distribution can be obtained analytically
from the uniform distribution with an appropriate transformation.  This
method uses one call to the random number generator.

   More complicated distributions are created by the
"acceptance-rejection" method, which compares the desired distribution
against a distribution which is similar and known analytically.  This
usually requires several samples from the generator.

   The functions described in this section are declared in
`gsl_randist.h'.

* Menu:

* The Gaussian Distribution::
* The Gaussian Tail Distribution::
* The Bivariate Gaussian Distribution::
* The Exponential Distribution::
* The Laplace Distribution::
* The Exponential Power Distribution::
* The Cauchy Distribution::
* The Rayleigh Distribution::
* The Rayleigh Tail Distribution::
* The Symmetric Levy Distribution::
* The Gamma Distribution::
* The Flat (Uniform) Distribution::
* The Lognormal Distribution::
* The Chi-squared Distribution::
* The F-distribution::
* The t-distribution::
* The Beta Distribution::
* The Logistic Distribution::
* The Pareto Distribution::
* The Spherical Distribution (2D & 3D)::
* The Weibull Distribution::
* The Type-1 Gumbel Distribution::
* The Type-2 Gumbel Distribution::
* General Discrete Distributions::
* The Poisson Distribution::
* The Bernoulli Distribution::
* The Binomial Distribution::
* The Negative Binomial Distribution::
* The Pascal Distribution::
* The Geometric Distribution::
* The Hypergeometric Distribution::
* The Logarithmic Distribution::
* Shuffling and Sampling::
* Random Number Distribution Examples::
* Random Number Distribution References and Further Reading::

