| 1 |
gezelter |
1741 |
/* -*- 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 "optimization/Armijo.hpp" |
| 21 |
|
|
#include "optimization/Method.hpp" |
| 22 |
|
|
#include "optimization/Problem.hpp" |
| 23 |
|
|
|
| 24 |
|
|
namespace QuantLib { |
| 25 |
|
|
|
| 26 |
|
|
RealType ArmijoLineSearch::operator()(Problem& P, |
| 27 |
|
|
EndCriteria::Type& ecType, |
| 28 |
|
|
const EndCriteria& endCriteria, |
| 29 |
|
|
const RealType t_ini) |
| 30 |
|
|
{ |
| 31 |
|
|
//OptimizationMethod& method = P.method(); |
| 32 |
|
|
Constraint& constraint = P.constraint(); |
| 33 |
|
|
succeed_=true; |
| 34 |
|
|
bool maxIter = false; |
| 35 |
gezelter |
1879 |
RealType t = t_ini; |
| 36 |
|
|
|
| 37 |
gezelter |
1741 |
|
| 38 |
|
|
RealType q0 = P.functionValue(); |
| 39 |
|
|
RealType qp0 = P.gradientNormValue(); |
| 40 |
|
|
|
| 41 |
|
|
qt_ = q0; |
| 42 |
|
|
qpt_ = (gradient_.empty()) ? qp0 : -P.DotProduct(gradient_,searchDirection_); |
| 43 |
|
|
|
| 44 |
|
|
// Initialize gradient |
| 45 |
|
|
gradient_ = DynamicVector<RealType>(P.currentValue().size()); |
| 46 |
|
|
// Compute new point |
| 47 |
|
|
xtd_ = P.currentValue(); |
| 48 |
|
|
t = update(xtd_, searchDirection_, t, constraint); |
| 49 |
|
|
// Compute function value at the new point |
| 50 |
|
|
qt_ = P.value (xtd_); |
| 51 |
|
|
|
| 52 |
|
|
// Enter in the loop if the criterion is not satisfied |
| 53 |
|
|
if ((qt_-q0) > -alpha_*t*qpt_) { |
| 54 |
gezelter |
1879 |
RealType qtold; |
| 55 |
|
|
size_t loopNumber = 0; |
| 56 |
gezelter |
1741 |
do { |
| 57 |
|
|
loopNumber++; |
| 58 |
|
|
// Decrease step |
| 59 |
|
|
t *= beta_; |
| 60 |
|
|
// Store old value of the function |
| 61 |
|
|
qtold = qt_; |
| 62 |
|
|
// New point value |
| 63 |
|
|
xtd_ = P.currentValue(); |
| 64 |
|
|
t = update(xtd_, searchDirection_, t, constraint); |
| 65 |
|
|
|
| 66 |
|
|
// Compute function value at the new point |
| 67 |
|
|
qt_ = P.value (xtd_); |
| 68 |
|
|
P.gradient (gradient_, xtd_); |
| 69 |
|
|
// and it squared norm |
| 70 |
|
|
maxIter = endCriteria.checkMaxIterations(loopNumber, ecType); |
| 71 |
|
|
} while ( |
| 72 |
|
|
(((qt_ - q0) > (-alpha_ * t * qpt_)) || |
| 73 |
|
|
((qtold - q0) <= (-alpha_ * t * qpt_ / beta_))) && |
| 74 |
|
|
(!maxIter)); |
| 75 |
|
|
} |
| 76 |
|
|
|
| 77 |
|
|
if (maxIter) |
| 78 |
|
|
succeed_ = false; |
| 79 |
|
|
|
| 80 |
|
|
// Compute new gradient |
| 81 |
|
|
P.gradient(gradient_, xtd_); |
| 82 |
|
|
// and it squared norm |
| 83 |
|
|
qpt_ = P.computeGradientNormValue(gradient_); |
| 84 |
|
|
//qpt_ = P.DotProduct(gradient_, gradient_); |
| 85 |
|
|
|
| 86 |
|
|
// Return new step value |
| 87 |
|
|
return t; |
| 88 |
|
|
} |
| 89 |
|
|
|
| 90 |
|
|
} |
