mirror of
https://github.com/ceres-solver/ceres-solver.git
synced 2026-08-29 16:40:38 +08:00
Sameer Agarwal
056ba9bb1d
This is to FirstOrderFunction, what AutoDiffCostFunction is to CostFunction. This allows users of GradientSolver to be able to define objective functions without requiring them to define the derivatives. The implementation uses the same Jet objects for computing the gradient as is used by AutoDiffCostFunction. Change-Id: Ide6e60532a3adab9be9899ba9b368dc267fd2dbb
116 lines
4.4 KiB
C++
116 lines
4.4 KiB
C++
// Ceres Solver - A fast non-linear least squares minimizer
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// Copyright 2015 Google Inc. All rights reserved.
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// http://ceres-solver.org/
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//
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// Redistribution and use in source and binary forms, with or without
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// modification, are permitted provided that the following conditions are met:
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//
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// * Redistributions of source code must retain the above copyright notice,
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// this list of conditions and the following disclaimer.
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// * Redistributions in binary form must reproduce the above copyright notice,
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// this list of conditions and the following disclaimer in the documentation
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// and/or other materials provided with the distribution.
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// * Neither the name of Google Inc. nor the names of its contributors may be
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// used to endorse or promote products derived from this software without
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// specific prior written permission.
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//
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// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
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// AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
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// IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
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// ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
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// LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
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// CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
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// SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
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// INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
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// CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
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// ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
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// POSSIBILITY OF SUCH DAMAGE.
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//
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// Author: sameeragarwal@google.com (Sameer Agarwal)
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#ifndef CERES_PUBLIC_GRADIENT_PROBLEM_H_
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#define CERES_PUBLIC_GRADIENT_PROBLEM_H_
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#include <memory>
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#include "ceres/internal/port.h"
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#include "ceres/first_order_function.h"
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#include "ceres/local_parameterization.h"
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namespace ceres {
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class FirstOrderFunction;
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// Instances of GradientProblem represent general non-linear
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// optimization problems that must be solved using just the value of
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// the objective function and its gradient. Unlike the Problem class,
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// which can only be used to model non-linear least squares problems,
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// instances of GradientProblem not restricted in the form of the
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// objective function.
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//
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// Structurally GradientProblem is a composition of a
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// FirstOrderFunction and optionally a LocalParameterization.
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//
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// The FirstOrderFunction is responsible for evaluating the cost and
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// gradient of the objective function.
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//
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// The LocalParameterization is responsible for going back and forth
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// between the ambient space and the local tangent space. (See
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// local_parameterization.h for more details). When a
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// LocalParameterization is not provided, then the tangent space is
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// assumed to coincide with the ambient Euclidean space that the
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// gradient vector lives in.
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//
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// Example usage:
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//
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// The following demonstrate the problem construction for Rosenbrock's function
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//
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// f(x,y) = (1-x)^2 + 100(y - x^2)^2;
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//
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// class Rosenbrock : public ceres::FirstOrderFunction {
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// public:
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// virtual ~Rosenbrock() {}
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//
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// virtual bool Evaluate(const double* parameters,
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// double* cost,
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// double* gradient) const {
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// const double x = parameters[0];
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// const double y = parameters[1];
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//
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// cost[0] = (1.0 - x) * (1.0 - x) + 100.0 * (y - x * x) * (y - x * x);
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// if (gradient != NULL) {
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// gradient[0] = -2.0 * (1.0 - x) - 200.0 * (y - x * x) * 2.0 * x;
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// gradient[1] = 200.0 * (y - x * x);
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// }
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// return true;
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// };
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//
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// virtual int NumParameters() const { return 2; };
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// };
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//
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// ceres::GradientProblem problem(new Rosenbrock());
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class CERES_EXPORT GradientProblem {
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public:
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// Takes ownership of the function.
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explicit GradientProblem(FirstOrderFunction* function);
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// Takes ownership of the function and the parameterization.
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GradientProblem(FirstOrderFunction* function,
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LocalParameterization* parameterization);
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int NumParameters() const;
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int NumLocalParameters() const;
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// This call is not thread safe.
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bool Evaluate(const double* parameters, double* cost, double* gradient) const;
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bool Plus(const double* x, const double* delta, double* x_plus_delta) const;
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private:
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std::unique_ptr<FirstOrderFunction> function_;
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std::unique_ptr<LocalParameterization> parameterization_;
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std::unique_ptr<double[]> scratch_;
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};
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} // namespace ceres
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#endif // CERES_PUBLIC_GRADIENT_PROBLEM_H_
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