/usr/include/ql/math/optimization/problem.hpp is in libquantlib0-dev 1.7.1-1.
This file is owned by root:root, with mode 0o644.
The actual contents of the file can be viewed below.
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/*
Copyright (C) 2007 Ferdinando Ametrano
Copyright (C) 2007 François du Vignaud
Copyright (C) 2001, 2002, 2003 Nicolas Di Césaré
This file is part of QuantLib, a free-software/open-source library
for financial quantitative analysts and developers - http://quantlib.org/
QuantLib is free software: you can redistribute it and/or modify it
under the terms of the QuantLib license. You should have received a
copy of the license along with this program; if not, please email
<quantlib-dev@lists.sf.net>. The license is also available online at
<http://quantlib.org/license.shtml>.
This program is distributed in the hope that it will be useful, but WITHOUT
ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
FOR A PARTICULAR PURPOSE. See the license for more details.
*/
/*! \file problem.hpp
\brief Abstract optimization problem class
*/
#ifndef quantlib_optimization_problem_h
#define quantlib_optimization_problem_h
#include <ql/math/optimization/method.hpp>
#include <ql/math/optimization/costfunction.hpp>
namespace QuantLib {
class Constraint;
//! Constrained optimization problem
/*! \warning The passed CostFunction and Constraint instances are
stored by reference. The user of this class must
make sure that they are not destroyed before the
Problem instance.
*/
class Problem {
public:
//! default constructor
Problem(CostFunction& costFunction,
Constraint& constraint,
const Array& initialValue = Array())
: costFunction_(costFunction), constraint_(constraint),
currentValue_(initialValue) {}
/*! \warning it does not reset the current minumum to any initial value
*/
void reset();
//! call cost function computation and increment evaluation counter
Real value(const Array& x);
//! call cost values computation and increment evaluation counter
Disposable<Array> values(const Array& x);
//! call cost function gradient computation and increment
// evaluation counter
void gradient(Array& grad_f,
const Array& x);
//! call cost function computation and it gradient
Real valueAndGradient(Array& grad_f,
const Array& x);
//! Constraint
Constraint& constraint() const { return constraint_; }
//! Cost function
CostFunction& costFunction() const { return costFunction_; }
void setCurrentValue(const Array& currentValue) {
currentValue_=currentValue;
}
//! current value of the local minimum
const Array& currentValue() const { return currentValue_; }
void setFunctionValue(Real functionValue) {
functionValue_=functionValue;
}
//! value of cost function
Real functionValue() const { return functionValue_; }
void setGradientNormValue(Real squaredNorm) {
squaredNorm_=squaredNorm;
}
//! value of cost function gradient norm
Real gradientNormValue() const { return squaredNorm_; }
//! number of evaluation of cost function
Integer functionEvaluation() const { return functionEvaluation_; }
//! number of evaluation of cost function gradient
Integer gradientEvaluation() const { return gradientEvaluation_; }
protected:
//! Unconstrained cost function
CostFunction& costFunction_;
//! Constraint
Constraint& constraint_;
//! current value of the local minimum
Array currentValue_;
//! function and gradient norm values at the curentValue_ (i.e. the last step)
Real functionValue_, squaredNorm_;
//! number of evaluation of cost function and its gradient
Integer functionEvaluation_, gradientEvaluation_;
};
// inline definitions
inline Real Problem::value(const Array& x) {
++functionEvaluation_;
return costFunction_.value(x);
}
inline Disposable<Array> Problem::values(const Array& x) {
++functionEvaluation_;
return costFunction_.values(x);
}
inline void Problem::gradient(Array& grad_f,
const Array& x) {
++gradientEvaluation_;
costFunction_.gradient(grad_f, x);
}
inline Real Problem::valueAndGradient(Array& grad_f,
const Array& x) {
++functionEvaluation_;
++gradientEvaluation_;
return costFunction_.valueAndGradient(grad_f, x);
}
inline void Problem::reset() {
functionEvaluation_ = gradientEvaluation_ = 0;
functionValue_ = squaredNorm_ = Null<Real>();
}
}
#endif
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