1/* -*- mode: c++; tab-width: 4; indent-tabs-mode: nil; c-basic-offset: 4 -*- */
2
3/*
4 Copyright (C) 2015 Cheng Li
5 This file is part of QuantLib, a free-software/open-source library
6 for financial quantitative analysts and developers - http://quantlib.org/
7
8 QuantLib is free software: you can redistribute it and/or modify it
9 under the terms of the QuantLib license. You should have received a
10 copy of the license along with this program; if not, please email
11 <quantlib-dev@lists.sf.net>. The license is also available online at
12 <http://quantlib.org/license.shtml>.
13
14 This program is distributed in the hope that it will be useful, but WITHOUT
15 ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
16 FOR A PARTICULAR PURPOSE. See the license for more details.
17*/
18
19#include <ql/math/optimization/goldstein.hpp>
20#include <ql/math/optimization/method.hpp>
21#include <ql/math/optimization/problem.hpp>
22#include <ql/math/comparison.hpp>
23
24namespace QuantLib {
25
26 Real GoldsteinLineSearch::operator()(Problem& P,
27 EndCriteria::Type& ecType,
28 const EndCriteria& endCriteria,
29 const Real t_ini)
30 {
31 Constraint& constraint = P.constraint();
32 succeed_=true;
33 bool maxIter = false;
34 Real t = t_ini;
35 Size loopNumber = 0;
36
37 Real q0 = P.functionValue();
38 Real qp0 = P.gradientNormValue();
39
40 Real tl = 0.0;
41 Real tr = 0.0;
42
43 qt_ = q0;
44 qpt_ = (gradient_.empty()) ? qp0 : -DotProduct(v1: gradient_,v2: searchDirection_);
45
46 // Initialize gradient
47 gradient_ = Array(P.currentValue().size());
48 // Compute new point
49 xtd_ = P.currentValue();
50 t = update(params&: xtd_, direction: searchDirection_, beta: t, constraint);
51 // Compute function value at the new point
52 qt_ = P.value (x: xtd_);
53
54 while ((qt_ - q0) < -beta_*t*qpt_ || (qt_ - q0) > -alpha_*t*qpt_) {
55 if ((qt_ - q0) > -alpha_*t*qpt_)
56 tr = t;
57 else
58 tl = t;
59 ++loopNumber;
60
61 // calculate the new step
62 if (close_enough(x: tr, y: 0.0))
63 t *= extrapolation_;
64 else
65 t = (tl + tr) / 2.0;
66
67 // New point value
68 xtd_ = P.currentValue();
69 t = update(params&: xtd_, direction: searchDirection_, beta: t, constraint);
70
71 // Compute function value at the new point
72 qt_ = P.value (x: xtd_);
73 P.gradient (grad_f&: gradient_, x: xtd_);
74 // and it squared norm
75 maxIter = endCriteria.checkMaxIterations(iteration: loopNumber, ecType);
76
77 if (maxIter)
78 break;
79 }
80
81 if (maxIter)
82 succeed_ = false;
83
84 // Compute new gradient
85 P.gradient(grad_f&: gradient_, x: xtd_);
86 // and it squared norm
87 qpt_ = DotProduct(v1: gradient_, v2: gradient_);
88
89 // Return new step value
90 return t;
91 }
92
93}
94

source code of quantlib/ql/math/optimization/goldstein.cpp