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/* -*- mode: c++; tab-width: 4; indent-tabs-mode: nil; c-basic-offset: 4 -*- */
/*
Copyright (C) 2001, 2002, 2003 Nicolas Di Césaré
Copyright (C) 2015 Peter Caspers
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>
#include <ql/math/matrix.hpp>
namespace QuantLib {
//! Cost function abstract class for optimization problem
class CostFunction {
public:
virtual ~CostFunction() = default;
//! method to overload to compute the cost function value in x
virtual Real value(const Array& x) const {
Array v = values(x);
std::transform(v.begin(), v.end(), v.begin(), [](Real x) -> Real { return x*x; });
return std::sqrt(std::accumulate(v.begin(), v.end(), Real(0.0)) /
static_cast<Real>(v.size()));
}
//! method to overload to compute the cost function values in x
virtual 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);
}
//! method to overload to compute J_f, the jacobian of
// the cost function with respect to x
virtual void jacobian(Matrix &jac, const Array &x) const {
Real eps = finiteDifferenceEpsilon();
Array xx(x), fp, fm;
for(Size i=0; i<x.size(); ++i) {
xx[i] += eps;
fp = values(xx);
xx[i] -= 2.0*eps;
fm = values(xx);
for(Size j=0; j<fp.size(); ++j) {
jac[j][i] = 0.5*(fp[j]-fm[j])/eps;
}
xx[i] = x[i];
}
}
//! method to overload to compute J_f, the jacobian of
// the cost function with respect to x and also the cost function
virtual Array valuesAndJacobian(Matrix &jac,
const Array &x) const {
jacobian(jac,x);
return values(x);
}
//! Default epsilon for finite difference method :
virtual Real finiteDifferenceEpsilon() const { return 1e-8; }
};
class ParametersTransformation {
public:
virtual ~ParametersTransformation() = default;
virtual Array direct(const Array& x) const = 0;
virtual Array inverse(const Array& x) const = 0;
};
}
#endif
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