// 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/
//
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// modification, are permitted provided that the following conditions are met:
//
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//   this list of conditions and the following disclaimer.
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//
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//
// Author: sameeragarwal@google.com (Sameer Agarwal)
//         keir@google.m (Keir Mierle)
//
// This is the interface through which the least squares solver accesses the
// residual and Jacobian of the least squares problem. Users are expected to
// subclass CostFunction to define their own terms in the least squares problem.
//
// It is recommended that users define templated residual functors for use as
// arguments for AutoDiffCostFunction (see autodiff_cost_function.h), instead of
// directly implementing the CostFunction interface. This often results in both
// shorter code and faster execution than hand-coded derivatives. However,
// specialized cases may demand direct implementation of the lower-level
// CostFunction interface; for example, this is true when calling legacy code
// which is not templated on numeric types.

#ifndef CERES_PUBLIC_COST_FUNCTION_H_
#define CERES_PUBLIC_COST_FUNCTION_H_

#include <vector>
#include "ceres/internal/macros.h"
#include "ceres/internal/port.h"
#include "ceres/types.h"

namespace ceres {

// This class implements the computation of the cost (a.k.a. residual) terms as
// a function of the input (control) variables, and is the interface for users
// to describe their least squares problem to Ceres. In other words, this is the
// modelling layer between users and the Ceres optimizer. The signature of the
// function (number and sizes of input parameter blocks and number of outputs)
// is stored in parameter_block_sizes_ and num_residuals_ respectively. User
// code inheriting from this class is expected to set these two members with the
// corresponding accessors. This information will be verified by the Problem
// when added with AddResidualBlock().
class CERES_EXPORT CostFunction {
 public:
  CostFunction() : num_residuals_(0) {}

  virtual ~CostFunction() {}

  // Inputs:
  //
  // parameters is an array of pointers to arrays containing the
  // various parameter blocks. parameters has the same number of
  // elements as parameter_block_sizes_.  Parameter blocks are in the
  // same order as parameter_block_sizes_.i.e.,
  //
  //   parameters_[i] = double[parameter_block_sizes_[i]]
  //
  // Outputs:
  //
  // residuals is an array of size num_residuals_.
  //
  // jacobians is an array of size parameter_block_sizes_ containing
  // pointers to storage for jacobian blocks corresponding to each
  // parameter block. Jacobian blocks are in the same order as
  // parameter_block_sizes, i.e. jacobians[i], is an
  // array that contains num_residuals_* parameter_block_sizes_[i]
  // elements. Each jacobian block is stored in row-major order, i.e.,
  //
  //   jacobians[i][r*parameter_block_size_[i] + c] =
  //                              d residual[r] / d parameters[i][c]
  //
  // If jacobians is NULL, then no derivatives are returned; this is
  // the case when computing cost only. If jacobians[i] is NULL, then
  // the jacobian block corresponding to the i'th parameter block must
  // not to be returned.
  //
  // The return value indicates whether the computation of the
  // residuals and/or jacobians was successful or not.
  //
  // This can be used to communicate numerical failures in jacobian
  // computations for instance.
  //
  // A more interesting and common use is to impose constraints on the
  // parameters. If the initial values of the parameter blocks satisfy
  // the constraints, then returning false whenever the constraints
  // are not satisfied will prevent the solver from moving into the
  // infeasible region. This is not a very sophisticated mechanism for
  // enforcing constraints, but is often good enough for things like
  // non-negativity constraints.
  //
  // Note that it is important that the initial values of the
  // parameter block must be feasible, otherwise the solver will
  // declare a numerical problem at iteration 0.
  virtual bool Evaluate(double const* const* parameters,
                        double* residuals,
                        double** jacobians) const = 0;

  const vector<int32>& parameter_block_sizes() const {
    return parameter_block_sizes_;
  }

  int num_residuals() const {
    return num_residuals_;
  }

 protected:
  vector<int32>* mutable_parameter_block_sizes() {
    return &parameter_block_sizes_;
  }

  void set_num_residuals(int num_residuals) {
    num_residuals_ = num_residuals;
  }

 private:
  // Cost function signature metadata: number of inputs & their sizes,
  // number of outputs (residuals).
  vector<int32> parameter_block_sizes_;
  int num_residuals_;
  CERES_DISALLOW_COPY_AND_ASSIGN(CostFunction);
};

}  // namespace ceres

#endif  // CERES_PUBLIC_COST_FUNCTION_H_
