DESCRIPTION

The random() function uses a nonlinear additive feedback random number generator employing a default table of size 31 long integers to return successive pseudo-random numbers in the range from 0 to RAND_MAX. The period of this random number generator is very large, approximately 16 * ((2^31) − 1).

The srandom() function sets its argument as the seed for a new sequence of pseudo-random integers to be returned by random(). These sequences are repeatable by calling srandom() with the same seed value. If no seed value is provided, the random() function is automatically seeded with a value of 1.

The initstate() function allows a state array state to be initialized for use by random(). The size of the state array n is used by initstate() to decide how sophisticated a random number generator it should use—the larger the state array, the better the random numbers will be. seed is the seed for the initialization, which specifies a starting point for the random number sequence, and provides for restarting at the same point.

The setstate() function changes the state array used by the random() function. The state array state is used for random number generation until the next call to initstate() or setstate(). state must first have been initialized using initstate() or be the result of a previous call of setstate().

RETURN VALUE

The random() function returns a value between 0 and RAND_MAX. The srandom() function returns no value. The initstate() function returns a pointer to the previous state array. The setstate() function returns a pointer to the previous state array, or NULL on error.

ERRORS

CONFORMING TO

4.3BSD, POSIX.1-2001.

NOTES

Current "optimal" values for the size of the state array n are 8, 32, 64, 128, and 256 bytes; other amounts will be rounded down to the nearest known amount. Using less than 8 bytes will cause an error.

This function should not be used in cases where multiple threads use random() and the behavior should be reproducible. Use random_r(3) for that purpose.

Random-number generation is a complex topic. Numerical Recipes in C: The Art of Scientific Computing (William H. Press, Brian P. Flannery, Saul A. Teukolsky, William T. Vetterling; New York: Cambridge University Press, 2007, 3rd ed.) provides an excellent discussion of practical random-number generation issues in Chapter 7 (Random Numbers).

For a more theoretical discussion which also covers many practical issues in depth, see Chapter 3 (Random Numbers) in Donald E. Knuth's The Art of Computer Programming, volume 2 (Seminumerical Algorithms), 2nd ed.; Reading, Massachusetts: Addison-Wesley Publishing Company, 1981.

SEE ALSO

drand48(3), rand(3), random_r(3), srand(3)