/usr/include/ql/math/optimization/costfunction.hpp is in libquantlib0-dev 1.4-2.
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) 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 costfunction.hpp
\brief Optimization cost function class
*/
#ifndef quantlib_optimization_costfunction_h
#define quantlib_optimization_costfunction_h
#include <ql/math/array.hpp>
namespace QuantLib {
//! Cost function abstract class for optimization problem
class CostFunction {
public:
virtual ~CostFunction() {}
//! method to overload to compute the cost function value in x
virtual Real value(const Array& x) const = 0;
//! method to overload to compute the cost function values in x
virtual Disposable<Array> values(const Array& x) const =0;
//! method to overload to compute grad_f, the first derivative of
// the cost function with respect to x
virtual void gradient(Array& grad, const Array& x) const {
Real eps = finiteDifferenceEpsilon(), fp, fm;
Array xx(x);
for (Size i=0; i<x.size(); i++) {
xx[i] += eps;
fp = value(xx);
xx[i] -= 2.0*eps;
fm = value(xx);
grad[i] = 0.5*(fp - fm)/eps;
xx[i] = x[i];
}
}
//! method to overload to compute grad_f, the first derivative of
// the cost function with respect to x and also the cost function
virtual Real valueAndGradient(Array& grad,
const Array& x) const {
gradient(grad, x);
return value(x);
}
//! Default epsilon for finite difference method :
virtual Real finiteDifferenceEpsilon() const { return 1e-8; }
};
class ParametersTransformation {
public:
virtual ~ParametersTransformation() {}
virtual Array direct(const Array& x) const = 0;
virtual Array inverse(const Array& x) const = 0;
};
}
#endif
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