namespace Eigen { /** \eigenManualPage TopicLinearAlgebraDecompositions Catalogue of dense decompositions This page presents a catalogue of the dense matrix decompositions offered by Eigen. For an introduction on linear solvers and decompositions, check this \link TutorialLinearAlgebra page \endlink. To get an overview of the true relative speed of the different decompositions, check this \link DenseDecompositionBenchmark benchmark \endlink. \section TopicLinAlgBigTable Catalogue of decompositions offered by Eigen

	Generic information, not Eigen-specific					Eigen-specific
Decomposition	Requirements on the matrix	Speed	Algorithm reliability and accuracy	Rank-revealing	Allows to compute (besides linear solving)	Linear solver provided by Eigen	Maturity of Eigen's implementation	Optimizations
PartialPivLU	Invertible	Fast	Depends on condition number	-	-	Yes	Excellent	Blocking, Implicit MT
FullPivLU	-	Slow	Proven	Yes	-	Yes	Excellent	-
HouseholderQR	-	Fast	Depends on condition number	-	Orthogonalization	Yes	Excellent	Blocking
ColPivHouseholderQR	-	Fast	Good	Yes	Orthogonalization	Yes	Excellent	Soon: blocking
FullPivHouseholderQR	-	Slow	Proven	Yes	Orthogonalization	Yes	Average	-
LLT	Positive definite	Very fast	Depends on condition number	-	-	Yes	Excellent	Blocking
LDLT	Positive or negative semidefinite¹	Very fast	Good	-	-	Yes	Excellent	Soon: blocking
\n Singular values and eigenvalues decompositions
BDCSVD (divide \& conquer)	-	One of the fastest SVD algorithms	Excellent	Yes	Singular values/vectors, least squares	Yes (and does least squares)	Excellent	Blocked bidiagonalization
JacobiSVD (two-sided)	-	Slow (but fast for small matrices)	Proven³	Yes	Singular values/vectors, least squares	Yes (and does least squares)	Excellent	R-SVD
SelfAdjointEigenSolver	Self-adjoint	Fast-average²	Good	Yes	Eigenvalues/vectors	-	Excellent	Closed forms for 2x2 and 3x3
ComplexEigenSolver	Square	Slow-very slow²	Depends on condition number	Yes	Eigenvalues/vectors	-	Average	-
EigenSolver	Square and real	Average-slow²	Depends on condition number	Yes	Eigenvalues/vectors	-	Average	-
GeneralizedSelfAdjointEigenSolver	Square	Fast-average²	Depends on condition number	-	Generalized eigenvalues/vectors	-	Good	-
\n Helper decompositions
RealSchur	Square and real	Average-slow²	Depends on condition number	Yes	-	-	Average	-
ComplexSchur	Square	Slow-very slow²	Depends on condition number	Yes	-	-	Average	-
Tridiagonalization	Self-adjoint	Fast	Good	-	-	-	Good	Soon: blocking
HessenbergDecomposition	Square	Average	Good	-	-	-	Good	Soon: blocking

\b Notes:

\b 1: There exist two variants of the LDLT algorithm. Eigen's one produces a pure diagonal D matrix, and therefore it cannot handle indefinite matrices, unlike Lapack's one which produces a block diagonal D matrix.
\b 2: Eigenvalues, SVD and Schur decompositions rely on iterative algorithms. Their convergence speed depends on how well the eigenvalues are separated.
\b 3: Our JacobiSVD is two-sided, making for proven and optimal precision for square matrices. For non-square matrices, we have to use a QR preconditioner first. The default choice, ColPivHouseholderQR, is already very reliable, but if you want it to be proven, use FullPivHouseholderQR instead.

\section TopicLinAlgTerminology Terminology

Selfadjoint: For a real matrix, selfadjoint is a synonym for symmetric. For a complex matrix, selfadjoint is a synonym for \em hermitian. More generally, a matrix \f$ A \f$ is selfadjoint if and only if it is equal to its adjoint \f$ A^* \f$. The adjoint is also called the \em conjugate \em transpose.
Positive/negative definite: A selfadjoint matrix \f$ A \f$ is positive definite if \f$ v^* A v > 0 \f$ for any non zero vector \f$ v \f$. In the same vein, it is negative definite if \f$ v^* A v < 0 \f$ for any non zero vector \f$ v \f$
Positive/negative semidefinite: A selfadjoint matrix \f$ A \f$ is positive semi-definite if \f$ v^* A v \ge 0 \f$ for any non zero vector \f$ v \f$. In the same vein, it is negative semi-definite if \f$ v^* A v \le 0 \f$ for any non zero vector \f$ v \f$
Blocking: Means the algorithm can work per block, whence guaranteeing a good scaling of the performance for large matrices.
Implicit Multi Threading (MT): Means the algorithm can take advantage of multicore processors via OpenMP. "Implicit" means the algortihm itself is not parallelized, but that it relies on parallelized matrix-matrix product routines.
Explicit Multi Threading (MT): Means the algorithm is explicitly parallelized to take advantage of multicore processors via OpenMP.
Meta-unroller: Means the algorithm is automatically and explicitly unrolled for very small fixed size matrices.

*/ }