mirror of
https://github.com/ceres-solver/ceres-solver.git
synced 2026-08-30 00:50:37 +08:00
Sameer Agarwal
17dccef91b
This has been a long requested feature so that users can minimize functions using numeric differentiation. As part of this, I have also redone rosenbrock.cc, which now has three variants. rosenbrock.cc now uses automatic differentiation. rosenbrock_numeric_diff.cc uses numeric differentiation. rosenbrock_analytic_diff.cc uses analytic derivatives. This is analogus to how the helloworld example code is structured. The tutorial for GradientProblemSolver has also been updated to reflect this. https://github.com/ceres-solver/ceres-solver/issues/691 Change-Id: Ib0fb9e35127fe4c8299d4793bea3558722c70dd7
80 lines
2.9 KiB
C++
80 lines
2.9 KiB
C++
// Ceres Solver - A fast non-linear least squares minimizer
|
|
// Copyright 2021 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)
|
|
|
|
#include "ceres/numeric_diff_first_order_function.h"
|
|
|
|
#include <memory>
|
|
|
|
#include "ceres/array_utils.h"
|
|
#include "ceres/first_order_function.h"
|
|
#include "gtest/gtest.h"
|
|
|
|
namespace ceres {
|
|
namespace internal {
|
|
|
|
class QuadraticCostFunctor {
|
|
public:
|
|
explicit QuadraticCostFunctor(double a) : a_(a) {}
|
|
bool operator()(const double* const x, double* cost) const {
|
|
cost[0] = x[0] * x[1] + x[2] * x[3] - a_;
|
|
return true;
|
|
}
|
|
|
|
private:
|
|
double a_;
|
|
};
|
|
|
|
TEST(NumericDiffFirstOrderFunction, BilinearDifferentiationTest) {
|
|
std::unique_ptr<FirstOrderFunction> function(
|
|
new NumericDiffFirstOrderFunction<QuadraticCostFunctor, CENTRAL, 4>(
|
|
new QuadraticCostFunctor(1.0)));
|
|
|
|
double parameters[4] = {1.0, 2.0, 3.0, 4.0};
|
|
double gradient[4];
|
|
double cost;
|
|
|
|
function->Evaluate(parameters, &cost, nullptr);
|
|
EXPECT_EQ(cost, 13.0);
|
|
|
|
cost = -1.0;
|
|
function->Evaluate(parameters, &cost, gradient);
|
|
|
|
EXPECT_EQ(cost, 13.0);
|
|
|
|
const double kTolerance = 1e-9;
|
|
EXPECT_NEAR(gradient[0], parameters[1], kTolerance);
|
|
EXPECT_NEAR(gradient[1], parameters[0], kTolerance);
|
|
EXPECT_NEAR(gradient[2], parameters[3], kTolerance);
|
|
EXPECT_NEAR(gradient[3], parameters[2], kTolerance);
|
|
}
|
|
|
|
} // namespace internal
|
|
} // namespace ceres
|