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// Ceres Solver - A fast non-linear least squares minimizer
// Copyright 2019 Google Inc. All rights reserved.
// http://ceres-solver.org/
//
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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.
// * Redistributions in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
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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 : 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);
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_