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NAG Toolbox

NAG Toolbox: nag_rand_init_skipahead (g05kj)

Purpose

nag_rand_init_skipahead (g05kj) allows for the generation of multiple, independent, sequences of pseudorandom numbers using the skip-ahead method.
The base pseudorandom number sequence defined by state is advanced nn places.

Syntax

[state, ifail] = g05kj(n, state)
[state, ifail] = nag_rand_init_skipahead(n, state)

Description

nag_rand_init_skipahead (g05kj) adjusts a base generator to allow multiple, independent, sequences of pseudorandom numbers to be generated via the skip-ahead method (see the G05 Chapter Introduction for details).
If, prior to calling nag_rand_init_skipahead (g05kj) the base generator defined by state would produce random numbers x1 , x2 , x3 , x1 , x2 , x3 , , then after calling nag_rand_init_skipahead (g05kj) the generator will produce random numbers xn + 1 , xn + 2 , xn + 3 , xn+1 , xn+2 , xn+3 , .
One of the initialization functions nag_rand_init_repeat (g05kf) (for a repeatable sequence if computed sequentially) or nag_rand_init_nonrepeat (g05kg) (for a non-repeatable sequence) must be called prior to the first call to nag_rand_init_skipahead (g05kj).
The skip-ahead algorithm can be used in conjunction with any of the six base generators discussed in Chapter G05.

References

Haramoto H, Matsumoto M, Nishimura T, Panneton F and L'Ecuyer P (2008) Efficient jump ahead for F2-linear random number generators INFORMS J. on Computing 20(3) 385–390
Knuth D E (1981) The Art of Computer Programming (Volume 2) (2nd Edition) Addison–Wesley

Parameters

Compulsory Input Parameters

1:     n – int64int32nag_int scalar
nn, the number of places to skip ahead.
Constraint: n0n0.
2:     state( : :) – int64int32nag_int array
Note: the actual argument supplied must be the array state supplied to the initialization routines nag_rand_init_repeat (g05kf) or nag_rand_init_nonrepeat (g05kg).
Contains information on the selected base generator and its current state.

Optional Input Parameters

None.

Input Parameters Omitted from the MATLAB Interface

None.

Output Parameters

1:     state( : :) – int64int32nag_int array
Note: the actual argument supplied must be the array state supplied to the initialization routines nag_rand_init_repeat (g05kf) or nag_rand_init_nonrepeat (g05kg).
Contains updated information on the state of the generator.
2:     ifail – int64int32nag_int scalar
ifail = 0ifail=0 unless the function detects an error (see [Error Indicators and Warnings]).

Error Indicators and Warnings

Errors or warnings detected by the function:
  ifail = 1ifail=1
On entry,n < 0n<0.
  ifail = 2ifail=2
On entry,state vector was not initialized or has been corrupted.
  ifail = 3ifail=3
On entry, cannot use the skip-ahead method with the base generator defined by state.
  ifail = 4ifail=4
On entry, the base generator is Mersenne Twister, but the state vector defined on initialization is not large enough to perform a skip-ahead. See the initialization function nag_rand_init_repeat (g05kf) or nag_rand_init_nonrepeat (g05kg).

Accuracy

Not applicable.

Further Comments

Calling nag_rand_init_skipahead (g05kj) and then generating a series of uniform values using nag_rand_dist_uniform01 (g05sa) is more efficient than, but equivalent to, calling nag_rand_dist_uniform01 (g05sa) and discarding the first nn values. This may not be the case for distributions other than the uniform, as some distributional generators require more than one uniform variate to generate a single draw from the required distribution.
To skip ahead k × mk×m places you can either
(a) call nag_rand_init_skipahead (g05kj) once with n = k × mn=k×m, or
(b) call nag_rand_init_skipahead (g05kj) kk times with n = mn=m, using the state vector output by the previous call as input to the next call
both approaches would result in the same sequence of values. When working in a multithreaded environment, where you want to generate (at most) mm values on each of KK threads, this would translate into either
(a) spawning the KK threads and calling nag_rand_init_skipahead (g05kj) once on each thread with n = (k1) × mn=(k-1)×m, where kk is a thread ID, taking a value between 11 and KK, or
(b) calling nag_rand_init_skipahead (g05kj) on a single thread with n = mn=m, spawning the KK threads and then calling nag_rand_init_skipahead (g05kj) a further k1k-1 times on each of the thread.
Due to the way skip ahead is implemented for the Mersenne Twister, approach (a) will tend to be more efficient if more than 30 threads are being used (i.e., K > 30K>30), otherwise approach (b) should probably be used. For all other base generators, approach (a) should be used. See the G05 Chapter Introduction for more details.

Example

function nag_rand_init_skipahead_example
% Initialize the seed
seed = [int64(1762543)];
% genid and subid identify the base generator
genid = int64(1);
subid =  int64(1);
lseed =  int64(1);
% n is the number of places to advance
n = int64(50);
% nv is the number of variates
nv = int64(5);

% Initialize the generator to a repeatable sequence
[state, ifail] = nag_rand_init_repeat(genid, subid, seed);
% Advance the sequence n places
[state, ifail] = nag_rand_init_skipahead(n, state);
% Generate nv variates from a uniform distribution
[state, x, ifail] = nag_rand_dist_uniform01(nv, state)
 

state =

                   17
                 1234
                    1
                    0
                19710
                22733
                17436
                15570
                17917
                13895
                19930
                    8
                    0
                 1234
                    1
                    1
                 1234


x =

    0.2071
    0.8413
    0.8817
    0.5494
    0.5248


ifail =

                    0


function g05kj_example
% Initialize the seed
seed = [int64(1762543)];
% genid and subid identify the base generator
genid = int64(1);
subid =  int64(1);
lseed =  int64(1);
% n is the number of places to advance
n = int64(50);
% nv is the number of variates
nv = int64(5);

% Initialize the generator to a repeatable sequence
[state, ifail] = g05kf(genid, subid, seed);
% Advance the sequence n places
[state, ifail] = g05kj(n, state);
% Generate nv variates from a uniform distribution
[state, x, ifail] = g05sa(nv, state)
 

state =

                   17
                 1234
                    1
                    0
                19710
                22733
                17436
                15570
                17917
                13895
                19930
                    8
                    0
                 1234
                    1
                    1
                 1234


x =

    0.2071
    0.8413
    0.8817
    0.5494
    0.5248


ifail =

                    0



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