File: numericaldifferentiation.hpp

package info (click to toggle)
quantlib 1.41-2
  • links: PTS, VCS
  • area: main
  • in suites: forky, sid
  • size: 41,480 kB
  • sloc: cpp: 400,885; makefile: 6,547; python: 214; sh: 150; lisp: 86
file content (86 lines) | stat: -rw-r--r-- 2,642 bytes parent folder | download
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
/* -*- mode: c++; tab-width: 4; indent-tabs-mode: nil; c-basic-offset: 4 -*- */

/*
 Copyright (C) 2015 Klaus Spanderen

 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
 <https://www.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 numericaldifferentiation.hpp
    \brief numerical differentiation of arbitrary order
           and on irregular grids
*/

#ifndef quantlib_numerical_differentiation_hpp
#define quantlib_numerical_differentiation_hpp

#include <ql/math/array.hpp>
#include <functional>

namespace QuantLib {

    //! Numerical Differentiation on arbitrarily spaced grids

    /*! References:

        B. Fornberg, 1988. Generation of Finite Difference Formulas
        on Arbitrarily Spaced Grids,
        http://amath.colorado.edu/faculty/fornberg/Docs/MathComp_88_FD_formulas.pdf
    */
    class NumericalDifferentiation {
      public:
        enum Scheme { Central, Backward, Forward };

        NumericalDifferentiation(std::function<Real(Real)> f,
                                 Size orderOfDerivative,
                                 Array x_offsets);

        NumericalDifferentiation(std::function<Real(Real)> f,
                                 Size orderOfDerivative,
                                 Real stepSize,
                                 Size steps,
                                 Scheme scheme);

        Real operator()(Real x) const;
        const Array& offsets() const;
        const Array& weights() const;

      private:
        const Array offsets_, w_;
        const std::function<Real(Real)> f_;
    };


    inline Real NumericalDifferentiation::operator()(Real x) const {
        Real s = 0.0;
        for (Size i=0; i < w_.size(); ++i) {
            if (std::fabs(w_[i]) > QL_EPSILON*QL_EPSILON) {
                s += w_[i] * f_(x+offsets_[i]);
            }
        }
        return s;
    }

    inline const Array& NumericalDifferentiation::weights() const {
        return w_;
    }

    inline const Array& NumericalDifferentiation::offsets() const {
        return offsets_;
    }
}


#endif