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+// Ceres Solver - A fast non-linear least squares minimizer
+// Copyright 2010, 2011, 2012 Google Inc. All rights reserved.
+// http://code.google.com/p/ceres-solver/
+//
+// 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)
+//         keir@google.com (Keir Mierle)
+//
+// The Problem object is used to build and hold least squares problems.
+
+#ifndef CERES_PUBLIC_PROBLEM_H_
+#define CERES_PUBLIC_PROBLEM_H_
+
+#include <cstddef>
+#include <map>
+#include <set>
+#include <vector>
+
+#include <glog/logging.h>
+#include "ceres/internal/macros.h"
+#include "ceres/internal/port.h"
+#include "ceres/internal/scoped_ptr.h"
+#include "ceres/types.h"
+
+namespace ceres {
+
+class CostFunction;
+class LossFunction;
+class LocalParameterization;
+
+namespace internal {
+class Preprocessor;
+class ProblemImpl;
+class ParameterBlock;
+class ResidualBlock;
+class SolverImpl;
+}  // namespace internal
+
+// A ResidualBlockId is a handle clients can use to delete residual
+// blocks after creating them. They are opaque for any purposes other
+// than that.
+typedef const internal::ResidualBlock* ResidualBlockId;
+
+// A class to represent non-linear least squares problems. Such
+// problems have a cost function that is a sum of error terms (known
+// as "residuals"), where each residual is a function of some subset
+// of the parameters. The cost function takes the form
+//
+//    N    1
+//   SUM  --- loss( || r_i1, r_i2,..., r_ik ||^2  ),
+//   i=1   2
+//
+// where
+//
+//   r_ij     is residual number i, component j; the residual is a
+//            function of some subset of the parameters x1...xk. For
+//            example, in a structure from motion problem a residual
+//            might be the difference between a measured point in an
+//            image and the reprojected position for the matching
+//            camera, point pair. The residual would have two
+//            components, error in x and error in y.
+//
+//   loss(y)  is the loss function; for example, squared error or
+//            Huber L1 loss. If loss(y) = y, then the cost function is
+//            non-robustified least squares.
+//
+// This class is specifically designed to address the important subset
+// of "sparse" least squares problems, where each component of the
+// residual depends only on a small number number of parameters, even
+// though the total number of residuals and parameters may be very
+// large. This property affords tremendous gains in scale, allowing
+// efficient solving of large problems that are otherwise
+// inaccessible.
+//
+// The canonical example of a sparse least squares problem is
+// "structure-from-motion" (SFM), where the parameters are points and
+// cameras, and residuals are reprojection errors. Typically a single
+// residual will depend only on 9 parameters (3 for the point, 6 for
+// the camera).
+//
+// To create a least squares problem, use the AddResidualBlock() and
+// AddParameterBlock() methods, documented below. Here is an example least
+// squares problem containing 3 parameter blocks of sizes 3, 4 and 5
+// respectively and two residual terms of size 2 and 6:
+//
+//   double x1[] = { 1.0, 2.0, 3.0 };
+//   double x2[] = { 1.0, 2.0, 3.0, 5.0 };
+//   double x3[] = { 1.0, 2.0, 3.0, 6.0, 7.0 };
+//
+//   Problem problem;
+//
+//   problem.AddResidualBlock(new MyUnaryCostFunction(...), x1);
+//   problem.AddResidualBlock(new MyBinaryCostFunction(...), x2, x3);
+//
+// Please see cost_function.h for details of the CostFunction object.
+class Problem {
+ public:
+  struct Options {
+    Options()
+        : cost_function_ownership(TAKE_OWNERSHIP),
+          loss_function_ownership(TAKE_OWNERSHIP),
+          local_parameterization_ownership(TAKE_OWNERSHIP) {}
+
+    // These flags control whether the Problem object owns the cost
+    // functions, loss functions, and parameterizations passed into
+    // the Problem. If set to TAKE_OWNERSHIP, then the problem object
+    // will delete the corresponding cost or loss functions on
+    // destruction. The destructor is careful to delete the pointers
+    // only once, since sharing cost/loss/parameterizations is
+    // allowed.
+    Ownership cost_function_ownership;
+    Ownership loss_function_ownership;
+    Ownership local_parameterization_ownership;
+  };
+
+  // The default constructor is equivalent to the
+  // invocation Problem(Problem::Options()).
+  Problem();
+  explicit Problem(const Options& options);
+
+  ~Problem();
+
+  // Add a residual block to the overall cost function. The cost
+  // function carries with it information about the sizes of the
+  // parameter blocks it expects. The function checks that these match
+  // the sizes of the parameter blocks listed in parameter_blocks. The
+  // program aborts if a mismatch is detected. loss_function can be
+  // NULL, in which case the cost of the term is just the squared norm
+  // of the residuals.
+  //
+  // The user has the option of explicitly adding the parameter blocks
+  // using AddParameterBlock. This causes additional correctness
+  // checking; however, AddResidualBlock implicitly adds the parameter
+  // blocks if they are not present, so calling AddParameterBlock
+  // explicitly is not required.
+  //
+  // The Problem object by default takes ownership of the
+  // cost_function and loss_function pointers. These objects remain
+  // live for the life of the Problem object. If the user wishes to
+  // keep control over the destruction of these objects, then they can
+  // do this by setting the corresponding enums in the Options struct.
+  //
+  // Note: Even though the Problem takes ownership of cost_function
+  // and loss_function, it does not preclude the user from re-using
+  // them in another residual block. The destructor takes care to call
+  // delete on each cost_function or loss_function pointer only once,
+  // regardless of how many residual blocks refer to them.
+  //
+  // Example usage:
+  //
+  //   double x1[] = {1.0, 2.0, 3.0};
+  //   double x2[] = {1.0, 2.0, 5.0, 6.0};
+  //   double x3[] = {3.0, 6.0, 2.0, 5.0, 1.0};
+  //
+  //   Problem problem;
+  //
+  //   problem.AddResidualBlock(new MyUnaryCostFunction(...), NULL, x1);
+  //   problem.AddResidualBlock(new MyBinaryCostFunction(...), NULL, x2, x1);
+  //
+  ResidualBlockId AddResidualBlock(CostFunction* cost_function,
+                                   LossFunction* loss_function,
+                                   const vector<double*>& parameter_blocks);
+
+  // Convenience methods for adding residuals with a small number of
+  // parameters. This is the common case. Instead of specifying the
+  // parameter block arguments as a vector, list them as pointers.
+  ResidualBlockId AddResidualBlock(CostFunction* cost_function,
+                                   LossFunction* loss_function,
+                                   double* x0);
+  ResidualBlockId AddResidualBlock(CostFunction* cost_function,
+                                   LossFunction* loss_function,
+                                   double* x0, double* x1);
+  ResidualBlockId AddResidualBlock(CostFunction* cost_function,
+                                   LossFunction* loss_function,
+                                   double* x0, double* x1, double* x2);
+  ResidualBlockId AddResidualBlock(CostFunction* cost_function,
+                                   LossFunction* loss_function,
+                                   double* x0, double* x1, double* x2,
+                                   double* x3);
+  ResidualBlockId AddResidualBlock(CostFunction* cost_function,
+                                   LossFunction* loss_function,
+                                   double* x0, double* x1, double* x2,
+                                   double* x3, double* x4);
+  ResidualBlockId AddResidualBlock(CostFunction* cost_function,
+                                   LossFunction* loss_function,
+                                   double* x0, double* x1, double* x2,
+                                   double* x3, double* x4, double* x5);
+
+  // Add a parameter block with appropriate size to the problem.
+  // Repeated calls with the same arguments are ignored. Repeated
+  // calls with the same double pointer but a different size results
+  // in undefined behaviour.
+  void AddParameterBlock(double* values, int size);
+
+  // Add a parameter block with appropriate size and parameterization
+  // to the problem. Repeated calls with the same arguments are
+  // ignored. Repeated calls with the same double pointer but a
+  // different size results in undefined behaviour.
+  void AddParameterBlock(double* values,
+                         int size,
+                         LocalParameterization* local_parameterization);
+
+  // Hold the indicated parameter block constant during optimization.
+  void SetParameterBlockConstant(double* values);
+
+  // Allow the indicated parameter to vary during optimization.
+  void SetParameterBlockVariable(double* values);
+
+  // Set the local parameterization for one of the parameter blocks.
+  // The local_parameterization is owned by the Problem by default. It
+  // is acceptable to set the same parameterization for multiple
+  // parameters; the destructor is careful to delete local
+  // parameterizations only once. The local parameterization can only
+  // be set once per parameter, and cannot be changed once set.
+  void SetParameterization(double* values,
+                           LocalParameterization* local_parameterization);
+
+  // Number of parameter blocks in the problem. Always equals
+  // parameter_blocks().size() and parameter_block_sizes().size().
+  int NumParameterBlocks() const;
+
+  // The size of the parameter vector obtained by summing over the
+  // sizes of all the parameter blocks.
+  int NumParameters() const;
+
+  // Number of residual blocks in the problem. Always equals
+  // residual_blocks().size().
+  int NumResidualBlocks() const;
+
+  // The size of the residual vector obtained by summing over the
+  // sizes of all of the residual blocks.
+  int NumResiduals() const;
+
+ private:
+  friend class internal::SolverImpl;
+  internal::scoped_ptr<internal::ProblemImpl> problem_impl_;
+  DISALLOW_COPY_AND_ASSIGN(Problem);
+};
+
+}  // namespace ceres
+
+#endif  // CERES_PUBLIC_PROBLEM_H_