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+// Ceres Solver - A fast non-linear least squares minimizer
+// Copyright 2015 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_GRADIENT_PROBLEM_H_
+#define CERES_PUBLIC_GRADIENT_PROBLEM_H_
+
+#include <memory>
+#include "ceres/internal/port.h"
+#include "ceres/local_parameterization.h"
+
+namespace ceres {
+
+class FirstOrderFunction;
+
+// Instances of GradientProblem represent general non-linear
+// optimization problems that must be solved using just the value of
+// the objective function and its gradient. Unlike the Problem class,
+// which can only be used to model non-linear least squares problems,
+// instances of GradientProblem not restricted in the form of the
+// objective function.
+//
+// Structurally GradientProblem is a composition of a
+// FirstOrderFunction and optionally a LocalParameterization.
+//
+// The FirstOrderFunction is responsible for evaluating the cost and
+// gradient of the objective function.
+//
+// The LocalParameterization is responsible for going back and forth
+// between the ambient space and the local tangent space. (See
+// local_parameterization.h for more details). When a
+// LocalParameterization is not provided, then the tangent space is
+// assumed to coincide with the ambient Euclidean space that the
+// gradient vector lives in.
+//
+// Example usage:
+//
+// The following demonstrate the problem construction for Rosenbrock's function
+//
+// f(x,y) = (1-x)^2 + 100(y - x^2)^2;
+//
+// class Rosenbrock : public ceres::FirstOrderFunction {
+// public:
+// virtual ~Rosenbrock() {}
+//
+// virtual bool Evaluate(const double* parameters,
+// double* cost,
+// double* gradient) const {
+// const double x = parameters[0];
+// const double y = parameters[1];
+//
+// cost[0] = (1.0 - x) * (1.0 - x) + 100.0 * (y - x * x) * (y - x * x);
+// if (gradient != NULL) {
+// gradient[0] = -2.0 * (1.0 - x) - 200.0 * (y - x * x) * 2.0 * x;
+// gradient[1] = 200.0 * (y - x * x);
+// }
+// return true;
+// };
+//
+// virtual int NumParameters() const { return 2; };
+// };
+//
+// ceres::GradientProblem problem(new Rosenbrock());
+class CERES_EXPORT GradientProblem {
+ public:
+ // Takes ownership of the function.
+ explicit GradientProblem(FirstOrderFunction* function);
+
+ // Takes ownership of the function and the parameterization.
+ GradientProblem(FirstOrderFunction* function,
+ LocalParameterization* parameterization);
+
+ int NumParameters() const;
+ int NumLocalParameters() const;
+
+ // This call is not thread safe.
+ bool Evaluate(const double* parameters, double* cost, double* gradient) const;
+ bool Plus(const double* x, const double* delta, double* x_plus_delta) const;
+
+ private:
+ std::unique_ptr<FirstOrderFunction> function_;
+ std::unique_ptr<LocalParameterization> parameterization_;
+ std::unique_ptr<double[]> scratch_;
+};
+
+// A FirstOrderFunction object implements the evaluation of a function
+// and its gradient.
+class CERES_EXPORT FirstOrderFunction {
+ public:
+ virtual ~FirstOrderFunction() {}
+ // cost is never NULL. gradient may be null.
+ virtual bool Evaluate(const double* const parameters,
+ double* cost,
+ double* gradient) const = 0;
+ virtual int NumParameters() const = 0;
+};
+
+} // namespace ceres
+
+#endif // CERES_PUBLIC_GRADIENT_PROBLEM_H_