/usr/include/ql/math/randomnumbers/sobolrsg.hpp is in libquantlib0-dev 1.7.1-1.
This file is owned by root:root, with mode 0o644.
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/*
Copyright (C) 2003, 2004 Ferdinando Ametrano
Copyright (C) 2006 Richard Gould
Copyright (C) 2007 Mark Joshi
This file is part of QuantLib, a free-software/open-source library
for financial quantitative analysts and developers - http://quantlib.org/
QuantLib is free software: you can redistribute it and/or modify it
under the terms of the QuantLib license. You should have received a
copy of the license along with this program; if not, please email
<quantlib-dev@lists.sf.net>. The license is also available online at
<http://quantlib.org/license.shtml>.
This program is distributed in the hope that it will be useful, but WITHOUT
ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
FOR A PARTICULAR PURPOSE. See the license for more details.
*/
/*! \file sobolrsg.hpp
\brief Sobol low-discrepancy sequence generator
*/
#ifndef quantlib_sobol_ld_rsg_hpp
#define quantlib_sobol_ld_rsg_hpp
#include <ql/methods/montecarlo/sample.hpp>
#include <vector>
namespace QuantLib {
//! Sobol low-discrepancy sequence generator
/*! A Gray code counter and bitwise operations are used for very
fast sequence generation.
The implementation relies on primitive polynomials modulo two
from the book "Monte Carlo Methods in Finance" by Peter
Jäckel.
21 200 primitive polynomials modulo two are provided in QuantLib.
Jäckel has calculated 8 129 334 polynomials: if you need that many
dimensions you can replace the primitivepolynomials.cpp file included
in QuantLib with the one provided in the CD of the "Monte Carlo
Methods in Finance" book.
The choice of initialization numbers (also know as free direction
integers) is crucial for the homogeneity properties of the sequence.
Sobol defines two homogeneity properties: Property A and Property A'.
The unit initialization numbers suggested in "Numerical
Recipes in C", 2nd edition, by Press, Teukolsky, Vetterling,
and Flannery (section 7.7) fail the test for Property A even
for low dimensions.
Bratley and Fox published coefficients of the free direction
integers up to dimension 40, crediting unpublished work of
Sobol' and Levitan. See Bratley, P., Fox, B.L. (1988)
"Algorithm 659: Implementing Sobol's quasirandom sequence
generator," ACM Transactions on Mathematical Software
14:88-100. These values satisfy Property A for d<=20 and d =
23, 31, 33, 34, 37; Property A' holds for d<=6.
Jäckel provides in his book (section 8.3) initialization
numbers up to dimension 32. Coefficients for d<=8 are the same
as in Bradley-Fox, so Property A' holds for d<=6 but Property
A holds for d<=32.
The implementation of Lemieux, Cieslak, and Luttmer includes
coefficients of the free direction integers up to dimension
360. Coefficients for d<=40 are the same as in Bradley-Fox.
For dimension 40<d<=360 the coefficients have
been calculated as optimal values based on the "resolution"
criterion. See "RandQMC user's guide - A package for
randomized quasi-Monte Carlo methods in C," by C. Lemieux,
M. Cieslak, and K. Luttmer, version January 13 2004, and
references cited there
(http://www.math.ucalgary.ca/~lemieux/randqmc.html).
The values up to d<=360 has been provided to the QuantLib team by
Christiane Lemieux, private communication, September 2004.
For more info on Sobol' sequences see also "Monte Carlo
Methods in Financial Engineering," by P. Glasserman, 2004,
Springer, section 5.2.3
The Joe--Kuo numbers and the Kuo numbers are due to Stephen Joe
and Frances Kuo.
S. Joe and F. Y. Kuo, Constructing Sobol sequences with better
two-dimensional projections, preprint Nov 22 2007
See http://web.maths.unsw.edu.au/~fkuo/sobol/ for more information.
The Joe-Kuo numbers are available under a BSD-style license
available at the above link.
Note that the Kuo numbers were generated to work with a
different ordering of primitive polynomials for the first 40
or so dimensions which is why we have the Alternative
Primitive Polynomials.
\test
- the correctness of the returned values is tested by
reproducing known good values.
- the correctness of the returned values is tested by checking
their discrepancy against known good values.
*/
class SobolRsg {
public:
typedef Sample<std::vector<Real> > sample_type;
enum DirectionIntegers {
Unit, Jaeckel, SobolLevitan, SobolLevitanLemieux,
JoeKuoD5, JoeKuoD6, JoeKuoD7,
Kuo, Kuo2, Kuo3 };
/*! \pre dimensionality must be <= PPMT_MAX_DIM */
SobolRsg(Size dimensionality,
unsigned long seed = 0,
DirectionIntegers directionIntegers = Jaeckel);
/*! skip to the n-th sample in the low-discrepancy sequence */
void skipTo(unsigned long n);
const std::vector<unsigned long>& nextInt32Sequence() const;
const SobolRsg::sample_type& nextSequence() const {
const std::vector<unsigned long>& v = nextInt32Sequence();
// normalize to get a double in (0,1)
for (Size k=0; k<dimensionality_; ++k)
sequence_.value[k] = v[k] * normalizationFactor_;
return sequence_;
}
const sample_type& lastSequence() const { return sequence_; }
Size dimension() const { return dimensionality_; }
private:
static const int bits_;
static const double normalizationFactor_;
Size dimensionality_;
mutable unsigned long sequenceCounter_;
mutable bool firstDraw_;
mutable sample_type sequence_;
mutable std::vector<unsigned long> integerSequence_;
std::vector<std::vector<unsigned long> > directionIntegers_;
};
}
#endif
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