// Ceres Solver - A fast non-linear least squares minimizer

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// Author: sameeragarwal@google.com (Sameer Agarwal)

//

// Cost term that implements a prior on a parameter block using a

// normal distribution.

#ifndef CERES_PUBLIC_NORMAL_PRIOR_H_

#define CERES_PUBLIC_NORMAL_PRIOR_H_

#include "ceres/cost_function.h"

#include "ceres/internal/eigen.h"

#include "ceres/internal/disable_warnings.h"

namespace ceres {

// Implements a cost function of the form

//

//   cost(x) = ||A(x - b)||^2

//

// where, the matrix A and the vector b are fixed and x is the

// variable. In case the user is interested in implementing a cost

// function of the form

//

//   cost(x) = (x - mu)^T S^{-1} (x - mu)

//

// where, mu is a vector and S is a covariance matrix, then, A =

// S^{-1/2}, i.e the matrix A is the square root of the inverse of the

// covariance, also known as the stiffness matrix. There are however

// no restrictions on the shape of A. It is free to be rectangular,

// which would be the case if the covariance matrix S is rank

// deficient.

class CERES_EXPORT NormalPrior: public CostFunction {

 public:

  // Check that the number of rows in the vector b are the same as the

  // number of columns in the matrix A, crash otherwise.

  NormalPrior(const Matrix& A, const Vector& b);

  virtual bool Evaluate(double const* const* parameters,

                        double* residuals,

                        double** jacobians) const;

 private:

  Matrix A_;

  Vector b_;

};

}  // namespace ceres

#include "ceres/internal/reenable_warnings.h"

#endif  // CERES_PUBLIC_NORMAL_PRIOR_H_