1/* -*- mode: c++; tab-width: 4; indent-tabs-mode: nil; c-basic-offset: 4 -*- */
2
3/*
4 Copyright (C) 2001, 2002, 2003 Nicolas Di Césaré
5
6 This file is part of QuantLib, a free-software/open-source library
7 for financial quantitative analysts and developers - http://quantlib.org/
8
9 QuantLib is free software: you can redistribute it and/or modify it
10 under the terms of the QuantLib license. You should have received a
11 copy of the license along with this program; if not, please email
12 <quantlib-dev@lists.sf.net>. The license is also available online at
13 <http://quantlib.org/license.shtml>.
14
15 This program is distributed in the hope that it will be useful, but WITHOUT
16 ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
17 FOR A PARTICULAR PURPOSE. See the license for more details.
18*/
19
20#include <ql/math/optimization/armijo.hpp>
21#include <ql/math/optimization/method.hpp>
22#include <ql/math/optimization/problem.hpp>
23
24namespace QuantLib {
25
26 Real ArmijoLineSearch::operator()(Problem& P,
27 EndCriteria::Type& ecType,
28 const EndCriteria& endCriteria,
29 const Real t_ini)
30 {
31 //OptimizationMethod& method = P.method();
32 Constraint& constraint = P.constraint();
33 succeed_=true;
34 bool maxIter = false;
35 Real qtold, t = t_ini;
36 Size loopNumber = 0;
37
38 Real q0 = P.functionValue();
39 Real qp0 = P.gradientNormValue();
40
41 qt_ = q0;
42 qpt_ = (gradient_.empty()) ? qp0 : -DotProduct(v1: gradient_,v2: searchDirection_);
43
44 // Initialize gradient
45 gradient_ = Array(P.currentValue().size());
46 // Compute new point
47 xtd_ = P.currentValue();
48 t = update(params&: xtd_, direction: searchDirection_, beta: t, constraint);
49 // Compute function value at the new point
50 qt_ = P.value (x: xtd_);
51
52 // Enter in the loop if the criterion is not satisfied
53 if ((qt_-q0) > -alpha_*t*qpt_) {
54 do {
55 loopNumber++;
56 // Decrease step
57 t *= beta_;
58 // Store old value of the function
59 qtold = qt_;
60 // New point value
61 xtd_ = P.currentValue();
62 t = update(params&: xtd_, direction: searchDirection_, beta: t, constraint);
63
64 // Compute function value at the new point
65 qt_ = P.value (x: xtd_);
66 P.gradient (grad_f&: gradient_, x: xtd_);
67 // and it squared norm
68 maxIter = endCriteria.checkMaxIterations(iteration: loopNumber, ecType);
69 } while (
70 (((qt_ - q0) > (-alpha_ * t * qpt_)) ||
71 ((qtold - q0) <= (-alpha_ * t * qpt_ / beta_))) &&
72 (!maxIter));
73 }
74
75 if (maxIter)
76 succeed_ = false;
77
78 // Compute new gradient
79 P.gradient(grad_f&: gradient_, x: xtd_);
80 // and it squared norm
81 qpt_ = DotProduct(v1: gradient_, v2: gradient_);
82
83 // Return new step value
84 return t;
85 }
86
87}
88

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