1 // Ceres Solver - A fast non-linear least squares minimizer
2 // Copyright 2017 Google Inc. All rights reserved.
3 // http://ceres-solver.org/
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29 // Author: sameeragarwal@google.com (Sameer Agarwal)
31 #ifndef CERES_INTERNAL_INVERT_PSD_MATRIX_H_
32 #define CERES_INTERNAL_INVERT_PSD_MATRIX_H_
34 #include "ceres/internal/eigen.h"
35 #include "glog/logging.h"
36 #include "Eigen/Dense"
41 // Helper routine to compute the inverse or pseudo-inverse of a
42 // symmetric positive semi-definite matrix.
44 // assume_full_rank controls whether a Cholesky factorization or an
45 // Singular Value Decomposition is used to compute the inverse and the
46 // pseudo-inverse respectively.
48 // The template parameter kSize can either be Eigen::Dynamic or a
49 // positive integer equal to the number of rows of m.
51 typename EigenTypes<kSize, kSize>::Matrix InvertPSDMatrix(
52 const bool assume_full_rank,
53 const typename EigenTypes<kSize, kSize>::Matrix& m) {
54 const int size = m.rows();
56 // If the matrix can be assumed to be full rank, then just use the
57 // Cholesky factorization to invert it.
58 if (assume_full_rank) {
59 return m.template selfadjointView<Eigen::Upper>().llt().solve(
60 Matrix::Identity(size, size));
63 Eigen::JacobiSVD<Matrix> svd(m, Eigen::ComputeThinU | Eigen::ComputeThinV);
64 const double tolerance =
65 std::numeric_limits<double>::epsilon() * size * svd.singularValues()(0);
67 return svd.matrixV() *
68 (svd.singularValues().array() > tolerance)
69 .select(svd.singularValues().array().inverse(), 0)
72 svd.matrixU().adjoint();
75 } // namespace internal
78 #endif // CERES_INTERNAL_INVERT_PSD_MATRIX_H_