| 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 |  |  | RealType qtold, t = t_ini; | 
| 36 |  |  | size_t loopNumber = 0; | 
| 37 |  |  |  | 
| 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 |  |  | 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(xtd_, searchDirection_, t, constraint); | 
| 63 |  |  |  | 
| 64 |  |  | // Compute function value at the new point | 
| 65 |  |  | qt_ = P.value (xtd_); | 
| 66 |  |  | P.gradient (gradient_, xtd_); | 
| 67 |  |  | // and it squared norm | 
| 68 |  |  | maxIter = endCriteria.checkMaxIterations(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(gradient_, xtd_); | 
| 80 |  |  | // and it squared norm | 
| 81 |  |  | qpt_ = P.computeGradientNormValue(gradient_); | 
| 82 |  |  | //qpt_ = P.DotProduct(gradient_, gradient_); | 
| 83 |  |  |  | 
| 84 |  |  | // Return new step value | 
| 85 |  |  | return t; | 
| 86 |  |  | } | 
| 87 |  |  |  | 
| 88 |  |  | } |