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root/OpenMD/branches/development/src/optimization/Armijo.cpp
Revision: 1741
Committed: Tue Jun 5 18:02:44 2012 UTC (12 years, 10 months ago) by gezelter
File size: 3226 byte(s)
Log Message:
Adding initial import of optimization library

File Contents

# User Rev Content
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     }

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