// 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/disable_warnings.h"
#include "ceres/internal/eigen.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 final : 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, Vector b);
  bool Evaluate(double const* const* parameters,
                double* residuals,
                double** jacobians) const override;

 private:
  Matrix A_;
  Vector b_;
};

}  // namespace ceres

#include "ceres/internal/reenable_warnings.h"

#endif  // CERES_PUBLIC_NORMAL_PRIOR_H_