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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
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// LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
// CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
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// POSSIBILITY OF SUCH DAMAGE.
//
// 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"
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 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
#endif // CERES_PUBLIC_NORMAL_PRIOR_H_