QuantLib: a free/open-source library for quantitative finance
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sobolrsg.hpp
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1/* -*- mode: c++; tab-width: 4; indent-tabs-mode: nil; c-basic-offset: 4 -*- */
2
3/*
4 Copyright (C) 2003, 2004 Ferdinando Ametrano
5 Copyright (C) 2006 Richard Gould
6 Copyright (C) 2007 Mark Joshi
7
8 This file is part of QuantLib, a free-software/open-source library
9 for financial quantitative analysts and developers - http://quantlib.org/
10
11 QuantLib is free software: you can redistribute it and/or modify it
12 under the terms of the QuantLib license. You should have received a
13 copy of the license along with this program; if not, please email
14 <quantlib-dev@lists.sf.net>. The license is also available online at
15 <http://quantlib.org/license.shtml>.
16
17 This program is distributed in the hope that it will be useful, but WITHOUT
18 ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
19 FOR A PARTICULAR PURPOSE. See the license for more details.
20*/
21
22/*! \file sobolrsg.hpp
23 \brief Sobol low-discrepancy sequence generator
24*/
25
26#ifndef quantlib_sobol_ld_rsg_hpp
27#define quantlib_sobol_ld_rsg_hpp
28
30#include <cstdint>
31#include <vector>
32
33namespace QuantLib {
34
35 //! Sobol low-discrepancy sequence generator
36 /*! A Gray code counter and bitwise operations are used for very
37 fast sequence generation.
38
39 The implementation relies on primitive polynomials modulo two
40 from the book "Monte Carlo Methods in Finance" by Peter
41 Jäckel.
42
43 21 200 primitive polynomials modulo two are provided in QuantLib.
44 Jäckel has calculated 8 129 334 polynomials: if you need that many
45 dimensions you can replace the primitivepolynomials.cpp file included
46 in QuantLib with the one provided in the CD of the "Monte Carlo
47 Methods in Finance" book.
48
49 The choice of initialization numbers (also know as free direction
50 integers) is crucial for the homogeneity properties of the sequence.
51 Sobol defines two homogeneity properties: Property A and Property A'.
52
53 The unit initialization numbers suggested in "Numerical
54 Recipes in C", 2nd edition, by Press, Teukolsky, Vetterling,
55 and Flannery (section 7.7) fail the test for Property A even
56 for low dimensions.
57
58 Bratley and Fox published coefficients of the free direction
59 integers up to dimension 40, crediting unpublished work of
60 Sobol' and Levitan. See Bratley, P., Fox, B.L. (1988)
61 "Algorithm 659: Implementing Sobol's quasirandom sequence
62 generator," ACM Transactions on Mathematical Software
63 14:88-100. These values satisfy Property A for d<=20 and d =
64 23, 31, 33, 34, 37; Property A' holds for d<=6.
65
66 Jäckel provides in his book (section 8.3) initialization
67 numbers up to dimension 32. Coefficients for d<=8 are the same
68 as in Bradley-Fox, so Property A' holds for d<=6 but Property
69 A holds for d<=32.
70
71 The implementation of Lemieux, Cieslak, and Luttmer includes
72 coefficients of the free direction integers up to dimension
73 360. Coefficients for d<=40 are the same as in Bradley-Fox.
74 For dimension 40<d<=360 the coefficients have
75 been calculated as optimal values based on the "resolution"
76 criterion. See "RandQMC user's guide - A package for
77 randomized quasi-Monte Carlo methods in C," by C. Lemieux,
78 M. Cieslak, and K. Luttmer, version January 13 2004, and
79 references cited there
80 (http://www.math.ucalgary.ca/~lemieux/randqmc.html).
81 The values up to d<=360 has been provided to the QuantLib team by
82 Christiane Lemieux, private communication, September 2004.
83
84 For more info on Sobol' sequences see also "Monte Carlo
85 Methods in Financial Engineering," by P. Glasserman, 2004,
86 Springer, section 5.2.3
87
88 The Joe--Kuo numbers and the Kuo numbers are due to Stephen Joe
89 and Frances Kuo.
90
91 S. Joe and F. Y. Kuo, Constructing Sobol sequences with better
92 two-dimensional projections, preprint Nov 22 2007
93
94 See http://web.maths.unsw.edu.au/~fkuo/sobol/ for more information.
95
96 The Joe-Kuo numbers are available under a BSD-style license
97 available at the above link.
98
99 Note that the Kuo numbers were generated to work with a
100 different ordering of primitive polynomials for the first 40
101 or so dimensions which is why we have the Alternative
102 Primitive Polynomials.
103
104 \test
105 - the correctness of the returned values is tested by
106 reproducing known good values.
107 - the correctness of the returned values is tested by checking
108 their discrepancy against known good values.
109 */
110 class SobolRsg {
111 public:
117 /*! The so called generating integer is chosen to be \f$\gamma(n) = n\f$ if useGrayCode is set to false and
118 \f$\gamma(n) = G(n)\f$ where \f$G(n)\f$ is the Gray code of \f$n\f$ otherwise. The Sobol integers are then
119 constructed using formula 8.20 resp. 8.23, see "Monte Carlo Methods in Finance" by Peter Jäckel. The default
120 is to use the Gray code since this allows a faster sequence generation. The Burley2020SobolRsg relies on an
121 underlying SobolRsg not using the Gray code on the other hand due to its specific way of constructing the
122 integer sequence.
123
124 \pre dimensionality must be <= PPMT_MAX_DIM
125 */
126 explicit SobolRsg(Size dimensionality,
127 unsigned long seed = 0,
128 DirectionIntegers directionIntegers = Jaeckel,
129 bool useGrayCode = true);
130 /*! skip to the n-th sample in the low-discrepancy sequence */
131 const std::vector<std::uint32_t>& skipTo(std::uint32_t n) const;
132 const std::vector<std::uint32_t>& nextInt32Sequence() const;
133
135 const std::vector<std::uint32_t>& v = nextInt32Sequence();
136 // normalize to get a double in (0,1)
137 for (Size k = 0; k < dimensionality_; ++k)
138 sequence_.value[k] = v[k] * (0.5 / (1UL << 31));
139 return sequence_;
140 }
141 const sample_type& lastSequence() const { return sequence_; }
142 Size dimension() const { return dimensionality_; }
143 private:
145 mutable std::uint32_t sequenceCounter_ = 0;
146 mutable bool firstDraw_ = true;
148 mutable std::vector<std::uint32_t> integerSequence_;
149 std::vector<std::vector<std::uint32_t>> directionIntegers_;
151 };
152
153}
154
155#endif
Sobol low-discrepancy sequence generator.
Definition: sobolrsg.hpp:110
const std::vector< std::uint32_t > & skipTo(std::uint32_t n) const
Definition: sobolrsg.cpp:78775
const std::vector< std::uint32_t > & nextInt32Sequence() const
Definition: sobolrsg.cpp:78807
Size dimension() const
Definition: sobolrsg.hpp:142
sample_type sequence_
Definition: sobolrsg.hpp:147
const SobolRsg::sample_type & nextSequence() const
Definition: sobolrsg.hpp:134
std::uint32_t sequenceCounter_
Definition: sobolrsg.hpp:145
std::vector< std::vector< std::uint32_t > > directionIntegers_
Definition: sobolrsg.hpp:149
const sample_type & lastSequence() const
Definition: sobolrsg.hpp:141
std::vector< std::uint32_t > integerSequence_
Definition: sobolrsg.hpp:148
Sample< std::vector< Real > > sample_type
Definition: sobolrsg.hpp:112
std::size_t Size
size of a container
Definition: types.hpp:58
Definition: any.hpp:35
ext::shared_ptr< BlackVolTermStructure > v
weighted sample
weighted sample
Definition: sample.hpp:35