/usr/include/roboptim/core/finite-difference-gradient.hxx is in libroboptim-core-dev 2.0-6.
This file is owned by root:root, with mode 0o644.
The actual contents of the file can be viewed below.
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//
// This file is part of the roboptim.
//
// roboptim is free software: you can redistribute it and/or modify
// it under the terms of the GNU Lesser General Public License as published by
// the Free Software Foundation, either version 3 of the License, or
// (at your option) any later version.
//
// roboptim 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
// GNU Lesser General Public License for more details.
//
// You should have received a copy of the GNU Lesser General Public License
// along with roboptim. If not, see <http://www.gnu.org/licenses/>.
#ifndef ROBOPTIM_CORE_FINITE_DIFFERENCE_GRADIENT_HXX
# define ROBOPTIM_CORE_FINITE_DIFFERENCE_GRADIENT_HXX
# include "boost/type_traits/is_same.hpp"
# include <boost/mpl/same_as.hpp>
namespace roboptim
{
template <typename T>
BadGradient<T>::BadGradient (const vector_t& x,
const gradient_t& analyticalGradient,
const gradient_t& finiteDifferenceGradient,
const value_type& threshold)
: std::runtime_error ("bad gradient"),
x_ (x),
analyticalGradient_ (analyticalGradient),
finiteDifferenceGradient_ (finiteDifferenceGradient),
maxDelta_ (),
maxDeltaComponent_ (),
threshold_ (threshold)
{
assert (analyticalGradient.size () == finiteDifferenceGradient.size ());
maxDelta_ = -std::numeric_limits<Function::value_type>::infinity ();
for (size_type i = 0; i < analyticalGradient.size (); ++i)
{
value_type delta =
std::fabs (analyticalGradient[i] - finiteDifferenceGradient[i]);
if (delta > maxDelta_)
{
maxDelta_ = delta;
maxDeltaComponent_ = i;
}
}
}
template <typename T>
BadGradient<T>::~BadGradient () throw ()
{}
template <typename T>
std::ostream&
BadGradient<T>::print (std::ostream& o) const throw ()
{
o << this->what () << incindent << iendl
<< "X: " << x_ << iendl
<< "Analytical gradient: " << analyticalGradient_ << iendl
<< "Finite difference gradient: " << finiteDifferenceGradient_
<< iendl
<< "Max. delta: " << maxDelta_ << iendl
<< "Max. delta in component: " << maxDeltaComponent_ << iendl
<< "Max. allowed delta: " << threshold_ << decindent;
return o;
}
template <typename T>
std::ostream&
operator<< (std::ostream& o, const BadGradient<T>& bg)
{
return bg.print (o);
}
template <typename T, typename FdgPolicy>
GenericFiniteDifferenceGradient<T, FdgPolicy>::GenericFiniteDifferenceGradient
(const GenericFunction<T>& adaptee,
typename GenericDifferentiableFunction<T>::value_type epsilon)
throw ()
: GenericDifferentiableFunction<T>
(adaptee.inputSize (), adaptee.outputSize ()),
FdgPolicy (),
adaptee_ (adaptee),
epsilon_ (epsilon),
xEps_ (adaptee.inputSize ())
{
// Avoid meaningless values for epsilon such as 0 or NaN.
assert (epsilon != 0. && epsilon == epsilon);
}
template <typename T, typename FdgPolicy>
GenericFiniteDifferenceGradient<
T, FdgPolicy>::~GenericFiniteDifferenceGradient () throw ()
{
}
template <typename T, typename FdgPolicy>
void
GenericFiniteDifferenceGradient<T, FdgPolicy>::impl_compute
(result_t& result, const argument_t& argument) const throw ()
{
adaptee_ (result, argument);
}
template <typename T, typename FdgPolicy>
void
GenericFiniteDifferenceGradient<T, FdgPolicy>::impl_gradient
(gradient_t& gradient,
const argument_t& argument,
size_type idFunction) const throw ()
{
#ifndef ROBOPTIM_DO_NOT_CHECK_ALLOCATION
Eigen::internal::set_is_malloc_allowed (true);
#endif //! ROBOPTIM_DO_NOT_CHECK_ALLOCATION
this->computeGradient (adaptee_, epsilon_, gradient,
argument, idFunction, xEps_);
}
template <typename T>
bool
checkGradient
(const GenericDifferentiableFunction<T>& function,
typename GenericDifferentiableFunction<T>::size_type functionId,
const typename GenericDifferentiableFunction<T>::vector_t& x,
typename GenericDifferentiableFunction<T>::value_type threshold)
throw ()
{
GenericFiniteDifferenceGradient<T> fdfunction (function);
typename GenericDifferentiableFunction<T>::gradient_t grad =
function.gradient (x, functionId);
typename GenericDifferentiableFunction<T>::gradient_t fdgrad =
fdfunction.gradient (x, functionId);
return grad.isApprox (fdgrad, threshold);
return true;
}
template <typename T>
void
checkGradientAndThrow
(const GenericDifferentiableFunction<T>& function,
typename GenericDifferentiableFunction<T>::size_type functionId,
const typename GenericDifferentiableFunction<T>::vector_t& x,
typename GenericDifferentiableFunction<T>::value_type threshold)
throw (BadGradient<T>)
{
GenericFiniteDifferenceGradient<T> fdfunction (function);
DifferentiableFunction::gradient_t grad =
function.gradient (x, functionId);
DifferentiableFunction::gradient_t fdgrad =
fdfunction.gradient (x, functionId);
if (!checkGradient (function, functionId, x, threshold))
throw BadGradient<T> (x, grad, fdgrad, threshold);
}
namespace detail
{
template <typename T>
void
compute_deriv (const GenericFunction<T>& adaptee,
typename GenericFunction<T>::size_type j,
double h,
double& result,
double& round,
double& trunc,
const typename GenericFunction<T>::argument_t& argument,
typename GenericFunction<T>::size_type idFunction,
typename GenericFunction<T>::argument_t& xEps);
/// Algorithm from the Gnu Scientific Library.
template <typename T>
void
compute_deriv (const GenericFunction<T>& adaptee,
typename GenericFunction<T>::size_type j,
double h,
double& result,
double& round,
double& trunc,
const typename GenericFunction<T>::argument_t& argument,
typename GenericFunction<T>::size_type idFunction,
typename GenericFunction<T>::argument_t& xEps)
{
/* Compute the derivative using the 5-point rule (x-h, x-h/2, x,
x+h/2, x+h). Note that the central point is not used.
Compute the error using the difference between the 5-point and
the 3-point rule (x-h,x,x+h). Again the central point is not
used. */
xEps = argument;
xEps[j] = argument[j] - h;
double fm1 = adaptee (xEps)[idFunction];
xEps[j] = argument[j] + h;
double fp1 = adaptee (xEps)[idFunction];
xEps[j] = argument[j] - (h / 2.);
double fmh = adaptee (xEps)[idFunction];
xEps[j] = argument[j] + (h / 2.);
double fph = adaptee (xEps)[idFunction];
double r3 = .5 * (fp1 - fm1);
double r5 = (4. / 3.) * (fph - fmh) - (1. / 3.) * r3;
double e3 = (std::fabs (fp1) + std::fabs (fm1))
* std::numeric_limits<double>::epsilon ();
double e5 = 2. * (std::fabs (fph) + std::fabs (fmh))
* std::numeric_limits<double>::epsilon () + e3;
/* The next term is due to finite precision in x+h = O (eps * x) */
double dy =
std::max (std::fabs (r3 / h), std::fabs (r5 / h))
* (std::fabs (argument[j]) / h)
* std::numeric_limits<double>::epsilon ();
/* The truncation error in the r5 approximation itself is O(h^4).
However, for safety, we estimate the error from r5-r3, which is
O(h^2). By scaling h we will minimise this estimated error, not
the actual truncation error in r5. */
result = r5 / h;
trunc = std::fabs ((r5 - r3) / h); // Estimated truncation error O(h^2)
round = std::fabs (e5 / h) + dy; // Rounding error (cancellations)
}
} // end of namespace detail.
namespace finiteDifferenceGradientPolicies
{
template <>
inline void
Simple<EigenMatrixSparse>::computeGradient
(const GenericFunction<EigenMatrixSparse>& adaptee,
value_type epsilon,
gradient_t& gradient,
const argument_t& argument,
size_type idFunction,
argument_t& xEps) const throw ()
{
assert (adaptee.outputSize () - idFunction > 0);
result_t res = adaptee (argument);
for (size_type j = 0; j < adaptee.inputSize (); ++j)
{
xEps = argument;
xEps[j] += epsilon;
result_t resEps = adaptee (xEps);
gradient.insert (j) =
(resEps[idFunction] - res[idFunction]) / epsilon;
}
}
template <typename T>
void
Simple<T>::computeGradient
(const GenericFunction<T>& adaptee,
value_type epsilon,
gradient_t& gradient,
const argument_t& argument,
size_type idFunction,
argument_t& xEps) const throw ()
{
assert (adaptee.outputSize () - idFunction > 0);
result_t res = adaptee (argument);
for (size_type j = 0; j < adaptee.inputSize (); ++j)
{
xEps = argument;
xEps[j] += epsilon;
typename GenericFunction<T>::result_t resEps = adaptee (xEps);
gradient (j) = (resEps[idFunction] - res[idFunction]) / epsilon;
}
}
template <>
inline void
FivePointsRule<EigenMatrixSparse>::computeGradient
(const GenericFunction<EigenMatrixSparse>& adaptee,
value_type epsilon,
gradient_t& gradient,
const argument_t& argument,
size_type idFunction,
argument_t& xEps) const throw ()
{
assert (adaptee.outputSize () - idFunction > 0);
value_type h = epsilon / 2.;
value_type r_0 = 0.;
value_type round = 0.;
value_type trunc = 0.;
value_type error = 0.;
for (size_type j = 0; j < argument.size (); ++j)
{
detail::compute_deriv (adaptee, j, h,
r_0, round, trunc,
argument, idFunction, xEps);
error = round + trunc;
if (round < trunc && (round > 0 && trunc > 0))
{
value_type r_opt = 0., round_opt = 0., trunc_opt = 0.,
error_opt = 0.;
/* Compute an optimised stepsize to minimize the total error,
using the scaling of the truncation error (O(h^2)) and
rounding error (O(1/h)). */
value_type h_opt =
h * std::pow (round / (2. * trunc), 1. / 3.);
detail::compute_deriv (adaptee, j, h_opt,
r_opt, round_opt, trunc_opt,
argument, idFunction,
xEps);
error_opt = round_opt + trunc_opt;
/* Check that the new error is smaller, and that the new
derivative is consistent with the error bounds of the
original estimate. */
if (error_opt < error && std::fabs (r_opt - r_0) < 4. * error)
{
r_0 = r_opt;
error = error_opt;
}
}
gradient.insert (j) = r_0;
}
}
template <typename T>
void
FivePointsRule<T>::computeGradient
(const GenericFunction<T>& adaptee,
value_type epsilon,
gradient_t& gradient,
const argument_t& argument,
size_type idFunction,
argument_t& xEps) const throw ()
{
assert (adaptee.outputSize () - idFunction > 0);
value_type h = epsilon / 2.;
value_type r_0 = 0.;
value_type round = 0.;
value_type trunc = 0.;
value_type error = 0.;
for (size_type j = 0; j < argument.size (); ++j)
{
detail::compute_deriv (adaptee, j, h,
r_0, round, trunc,
argument, idFunction, xEps);
error = round + trunc;
if (round < trunc && (round > 0 && trunc > 0))
{
value_type r_opt = 0., round_opt = 0., trunc_opt = 0.,
error_opt = 0.;
/* Compute an optimised stepsize to minimize the total error,
using the scaling of the truncation error (O(h^2)) and
rounding error (O(1/h)). */
value_type h_opt =
h * std::pow (round / (2. * trunc), 1. / 3.);
detail::compute_deriv (adaptee, j, h_opt,
r_opt, round_opt, trunc_opt,
argument, idFunction,
xEps);
error_opt = round_opt + trunc_opt;
/* Check that the new error is smaller, and that the new
derivative is consistent with the error bounds of the
original estimate. */
if (error_opt < error && std::fabs (r_opt - r_0) < 4. * error)
{
r_0 = r_opt;
error = error_opt;
}
}
gradient[j] = r_0;
}
}
} // end of namespace finiteDifferenceGradientPolicies.
} // end of namespace roboptim
#endif //! ROBOPTIM_CORE_FINITE_DIFFERENCE_GRADIENT_HXX
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