File sparse_schur_solver.h#
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namespace sym
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template<typename _MatrixType>
class SparseSchurSolver# - #include <sparse_schur_solver.h>
A solver for factorizing and solving positive definite matrices with a large block-diagonal component
This includes matrices typically encountered in SfM, where the landmarks-to-landmarks block of the Hessian is block diagonal (there are no cross-terms between different landmarks)
We should have a matrix A of structure
A = ( B E ) ( E^T C )
with C in
R^{C_dim x C_dim}
block diagonal.The full problem is A x = b, which we can break down the same way as
( B E ) ( y ) = ( v ) ( E^T C ) ( z ) ( w )
We then have two equations:
B y + E z = v E^T y + C z = w
C is block diagonal and therefore easy to invert, so we can write
z = C^{-1} (w - E^T y)
Plugging this into the first equation, we can eliminate z:
B y + E C^{-1} (w - E^T y) = v => B y + E C^{-1} w - E C^{-1} E^T y = v => (B - E C^{-1} E^T) y = v - E C^{-1} w
Defining the Schur complement
S = B - E C^{-1} E^T
, we haveS y = v - E C^{-1} w
So, we can form S and use the above equation to solve for y. Once we have y, we can use the equation for z above to solve for z, and we’re done.
See http://ceres-solver.org/nnls_solving.html#dense-schur-sparse-schur
Public Types
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using MatrixType = _MatrixType#
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using Scalar = typename MatrixType::Scalar#
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using SMatrixSolverType = SparseCholeskySolver<MatrixType, Eigen::Lower>#
Public Functions
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inline SparseSchurSolver(const typename SMatrixSolverType::Ordering &ordering = Eigen::MetisOrdering<typename SMatrixSolverType::StorageIndex>())#
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inline bool IsInitialized() const#
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void ComputeSymbolicSparsity(const MatrixType &A, int C_dim)#
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void Factorize(const MatrixType &A)#
Private Members
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bool is_initialized_#
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SparsityInformation sparsity_information_#
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FactorizationData factorization_data_#
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SMatrixSolverType S_solver_#
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struct FactorizationData#
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using MatrixType = _MatrixType#
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template<typename _MatrixType>