g05ync initializes a scrambled quasi-random generator prior to calling g05yjc, g05ykc or g05ymc. It must be preceded by a call to one of the pseudorandom initialization functions g05kfc or g05kgc.

2 Specification

#include <nag.h>

void	g05ync (Nag_QuasiRandom_Sequence genid, Nag_QuasiRandom_Scrambling stype, Integer idim, Integer iref[], Integer liref, Integer iskip, Integer nsdigi, Integer state[], NagError *fail)

The function may be called by the names: g05ync, nag_rand_quasi_init_scrambled or nag_quasi_init_scrambled.

3 Description

g05ync selects a quasi-random number generator through the input value of genid, a method of scrambling through the input value of stype and initializes the iref communication array for use in the functions g05yjc, g05ykc or g05ymc.

Scrambled quasi-random sequences are an extension of standard quasi-random sequences that attempt to eliminate the bias inherent in a quasi-random sequence whilst retaining the low-discrepancy properties. The use of a scrambled sequence allows error estimation of Monte Carlo results by performing a number of iterates and computing the variance of the results.

This implementation of scrambled quasi-random sequences is based on TOMS Algorithm 823 and details can be found in the accompanying paper, Hong and Hickernell (2003). Three methods of scrambling are supplied; the first a restricted form of Owen's scrambling (Owen (1995)), the second based on the method of Faure and Tezuka (2000) and the last method combines the first two.

Scrambled versions of the Niederreiter sequence and two sets of Sobol sequences are provided. The first Sobol sequence is obtained using

genid = Nag_QuasiRandom_Sobol

. The first

10000

direction numbers for this sequence are based on the work of Joe and Kuo (2008). For dimensions greater than

10000

, the direction numbers are randomly generated using the pseudorandom generator specified in state (see Jäckel (2002) for details). The second Sobol sequence is obtained using

genid = Nag_QuasiRandom_SobolA659

and referred to in the documentation as ‘Sobol (A659)’. The first

1111

direction numbers for this sequence are based on Algorithm 659 of Bratley and Fox (1988) with the extension proposed by Joe and Kuo (2003). For dimensions greater than

1111

the direction numbers are once again randomly generated. The Niederreiter sequence is obtained by setting

genid = Nag_QuasiRandom_Nied

4 References

Bratley P and Fox B L (1988) Algorithm 659: implementing Sobol's quasirandom sequence generator ACM Trans. Math. Software 14(1) 88–100

Faure H and Tezuka S (2000) Another random scrambling of digital (t,s)-sequences Monte Carlo and Quasi-Monte Carlo Methods Springer-Verlag, Berlin, Germany (eds K T Fang, F J Hickernell and H Niederreiter)

Hong H S and Hickernell F J (2003) Algorithm 823: implementing scrambled digital sequences ACM Trans. Math. Software 29:2 95–109

Jäckel P (2002) Monte Carlo Methods in Finance Wiley Finance Series, John Wiley and Sons, England

Joe S and Kuo F Y (2003) Remark on Algorithm 659: implementing Sobol's quasirandom sequence generator ACM Trans. Math. Software (TOMS) 29 49–57

Joe S and Kuo F Y (2008) Constructing Sobol sequences with better two-dimensional projections SIAM J. Sci. Comput. 30 2635–2654

Niederreiter H (1988) Low-discrepancy and low dispersion sequences Journal of Number Theory 30 51–70

Owen A B (1995) Randomly permuted (t,m,s)-nets and (t,s)-sequences Monte Carlo and Quasi-Monte Carlo Methods in Scientific Computing, Lecture Notes in Statistics 106 Springer-Verlag, New York, NY 299–317 (eds H Niederreiter and P J-S Shiue)

5 Arguments

1: $genid$ – Nag_QuasiRandom_Sequence Input

On entry: must identify the quasi-random generator to use.

$genid = Nag_QuasiRandom_Sobol$: Sobol generator.
$genid = Nag_QuasiRandom_SobolA659$: Sobol (A659) generator.
$genid = Nag_QuasiRandom_Nied$: Niederreiter generator.

Constraint:

genid = Nag_QuasiRandom_Sobol

Nag_QuasiRandom_SobolA659

Nag_QuasiRandom_Nied

2: $stype$ – Nag_QuasiRandom_Scrambling Input

On entry: must identify the scrambling method to use.

$stype = Nag_NoScrambling$: No scrambling. This is equivalent to calling g05ylc.
$stype = Nag_OwenLike$: Owen like scrambling.
$stype = Nag_FaureTezuka$: Faure–Tezuka scrambling.
$stype = Nag_OwenFaureTezuka$: Owen and Faure–Tezuka scrambling.

Constraint:

stype = Nag_NoScrambling

Nag_OwenLike

Nag_FaureTezuka

Nag_OwenFaureTezuka

3: $idim$ – Integer Input

On entry: the number of dimensions required.

Constraints:

if $genid = Nag_QuasiRandom_Sobol$ , $1 \leq idim \leq 50000$ ;
if $genid = Nag_QuasiRandom_SobolA659$ , $1 \leq idim \leq 50000$ ;
if $genid = Nag_QuasiRandom_Nied$ , $1 \leq idim \leq 318$ .

4: $iref [liref]$ – Integer Communication Array

On exit: contains initialization information for use by the generator functions g05yjc, g05ykc and g05ymc. iref must not be altered in any way between initialization and calls of the generator functions.

5: $liref$ – Integer Input

On entry: the dimension of the array iref.

Constraint:

liref \geq 33 \times idim + 7

6: $iskip$ – Integer Input

On entry: the number of terms of the sequence to skip on initialization for the Sobol and Niederreiter generators.

Constraint:

0 \leq iskip \leq 2^{30}

7: $nsdigi$ – Integer Input

On entry: controls the number of digits (bits) to scramble when

genid = Nag_QuasiRandom_Sobol

Nag_QuasiRandom_SobolA659

, otherwise nsdigi is ignored. If

nsdigi < 1

nsdigi > 30

then all the digits are scrambled.

8: $state [\dim]$ – Integer Communication Array

Note: the dimension,

\dim

, of this array is dictated by the requirements of associated functions that must have been previously called. This array MUST be the same array passed as argument state in the previous call to nag_rand_init_repeatable (g05kfc) or nag_rand_init_nonrepeatable (g05kgc).

On entry: contains information on the selected base generator and its current state.

On exit: contains updated information on the state of the generator.

9: $fail$ – NagError * Input/Output

The NAG error argument (see Section 7 in the Introduction to the NAG Library CL Interface).

6 Error Indicators and Warnings

NE_ALLOC_FAIL: Dynamic memory allocation failed.
See Section 3.1.2 in the Introduction to the NAG Library CL Interface for further information.
NE_BAD_PARAM: On entry, argument $⟨ value ⟩$ had an illegal value.
NE_INT: On entry, $idim = ⟨ value ⟩$ .
Constraint: $1 \leq idim \leq ⟨ value ⟩$ .

On entry, $iskip = ⟨ value ⟩$ .
Constraint: $0 \leq iskip \leq 2^{30}$ .

On entry, $liref = ⟨ value ⟩$ .
Constraint: $liref \geq 32 \times idim + 7$ .
NE_INTERNAL_ERROR: An internal error has occurred in this function. Check the function call and any array sizes. If the call is correct then please contact NAG for assistance.
See Section 7.5 in the Introduction to the NAG Library CL Interface for further information.
NE_INVALID_STATE: On entry, state vector has been corrupted or not initialized.
NE_NO_LICENCE: Your licence key may have expired or may not have been installed correctly.
See Section 8 in the Introduction to the NAG Library CL Interface for further information.

7 Accuracy

Not applicable.

8 Parallelism and Performance

Background information to multithreading can be found in the Multithreading documentation.

g05ync is threaded by NAG for parallel execution in multithreaded implementations of the NAG Library.

Please consult the X06 Chapter Introduction for information on how to control and interrogate the OpenMP environment used within this function. Please also consult the Users' Note for your implementation for any additional implementation-specific information.

9 Further Comments

The additional computational cost in using a scrambled quasi-random sequence over a non-scrambled one comes entirely during the initialization. Once g05ync has been called the computational cost of generating a scrambled sequence and a non-scrambled one is identical.