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// Ceres Solver - A fast non-linear least squares minimizer
// Copyright 2019 Google Inc. All rights reserved.
// http://ceres-solver.org/
//
// Redistribution and use in source and binary forms, with or without
// modification, are permitted provided that the following conditions are met:
//
// * Redistributions of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
// * Redistributions in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
// * Neither the name of Google Inc. nor the names of its contributors may be
// used to endorse or promote products derived from this software without
// specific prior written permission.
//
// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
// AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
// IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
// ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
// LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
// CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
// SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
// INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
// CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
// ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
// POSSIBILITY OF SUCH DAMAGE.
//
// Author: sameeragarwal@google.com (Sameer Agarwal)
#ifndef CERES_PUBLIC_NUMERIC_DIFF_FIRST_ORDER_FUNCTION_H_
#define CERES_PUBLIC_NUMERIC_DIFF_FIRST_ORDER_FUNCTION_H_
#include <algorithm>
#include <memory>
#include "ceres/first_order_function.h"
#include "ceres/internal/eigen.h"
#include "ceres/internal/fixed_array.h"
#include "ceres/internal/numeric_diff.h"
#include "ceres/internal/parameter_dims.h"
#include "ceres/internal/variadic_evaluate.h"
#include "ceres/numeric_diff_options.h"
#include "ceres/types.h"
namespace ceres {
// Creates FirstOrderFunctions as needed by the GradientProblem
// framework, with gradients computed via numeric differentiation. For
// more information on numeric differentiation, see the wikipedia
// article at https://en.wikipedia.org/wiki/Numerical_differentiation
//
// To get an numerically differentiated cost function, you must define
// a class with an operator() (a functor) that computes the cost.
//
// The function must write the computed value in the last argument
// (the only non-const one) and return true to indicate success.
//
// For example, consider a scalar error e = x'y - a, where both x and y are
// two-dimensional column vector parameters, the prime sign indicates
// transposition, and a is a constant.
//
// To write an numerically-differentiable cost function for the above model,
// first define the object
//
// class QuadraticCostFunctor {
// public:
// explicit QuadraticCostFunctor(double a) : a_(a) {}
// bool operator()(const double* const xy, double* cost) const {
// constexpr int kInputVectorLength = 2;
// const double* const x = xy;
// const double* const y = xy + kInputVectorLength;
// *cost = x[0] * y[0] + x[1] * y[1] - a_;
// return true;
// }
//
// private:
// double a_;
// };
//
//
// Note that in the declaration of operator() the input parameters xy
// come first, and are passed as const pointers to array of
// doubles. The output cost is the last parameter.
//
// Then given this class definition, the numerically differentiated
// first order function with central differences used for computing the
// derivative can be constructed as follows.
//
// FirstOrderFunction* function
// = new NumericDiffFirstOrderFunction<MyScalarCostFunctor, CENTRAL, 4>(
// new QuadraticCostFunctor(1.0)); ^ ^ ^
// | | |
// Finite Differencing Scheme -+ | |
// Dimension of xy ------------------------+
//
//
// In the instantiation above, the template parameters following
// "QuadraticCostFunctor", "CENTRAL, 4", describe the finite
// differencing scheme as "central differencing" and the functor as
// computing its cost from a 4 dimensional input.
template <typename FirstOrderFunctor,
NumericDiffMethodType method,
int kNumParameters>
class NumericDiffFirstOrderFunction final : public FirstOrderFunction {
public:
explicit NumericDiffFirstOrderFunction(
FirstOrderFunctor* functor,
Ownership ownership = TAKE_OWNERSHIP,
const NumericDiffOptions& options = NumericDiffOptions())
: functor_(functor), ownership_(ownership), options_(options) {
static_assert(kNumParameters > 0, "kNumParameters must be positive");
}
~NumericDiffFirstOrderFunction() override {
if (ownership_ != TAKE_OWNERSHIP) {
functor_.release();
}
}
bool Evaluate(const double* const parameters,
double* cost,
double* gradient) const override {
using ParameterDims = internal::StaticParameterDims<kNumParameters>;
constexpr int kNumResiduals = 1;
// Get the function value (cost) at the the point to evaluate.
if (!internal::VariadicEvaluate<ParameterDims>(
*functor_, ¶meters, cost)) {
return false;
}
if (gradient == nullptr) {
return true;
}
// Create a copy of the parameters which will get mutated.
internal::FixedArray<double, 32> parameters_copy(kNumParameters);
std::copy_n(parameters, kNumParameters, parameters_copy.data());
double* parameters_ptr = parameters_copy.data();
internal::EvaluateJacobianForParameterBlocks<
ParameterDims>::template Apply<method, kNumResiduals>(functor_.get(),
cost,
options_,
kNumResiduals,
¶meters_ptr,
&gradient);
return true;
}
int NumParameters() const override { return kNumParameters; }
const FirstOrderFunctor& functor() const { return *functor_; }
private:
std::unique_ptr<FirstOrderFunctor> functor_;
Ownership ownership_;
NumericDiffOptions options_;
};
} // namespace ceres
#endif // CERES_PUBLIC_NUMERIC_DIFF_FIRST_ORDER_FUNCTION_H_
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