Interface  Description 

Bidiagonal<N extends Number> 
A general matrix [A] can be factorized by similarity transformations into the form [A]=[Q1][D][Q2]
^{1} where:
[A] (mbyn) is any, real or complex, matrix
[D] (rbyr) or (mbyn) is, upper or lower, bidiagonal
[Q1] (mbyr) or (mbym) is orthogonal
[Q2] (nbyr) or (nbyn) is orthogonal
r = min(m,n)

Bidiagonal.Factory<N extends Number>  
Cholesky<N extends Number> 
Cholesky: [A] = [L][L]^{H} (or [R]^{H}[R])

Cholesky.Factory<N extends Number>  
DecompositionStore<N extends Number> 
Only classes that will act as a delegate to a MatrixDecomposition implementation from this
package should implement this interface.

Eigenvalue<N extends Number> 
[A] = [V][D][V]^{1} ([A][V] = [V][D])
[A] = any square matrix.
[V] = contains the eigenvectors as columns.
[D] = a diagonal matrix with the eigenvalues on the diagonal (possibly in blocks).

Eigenvalue.Factory<N extends Number>  
Hessenberg<N extends Number> 
Hessenberg: [A] = [Q][H][Q]^{T} A general square matrix [A] can be decomposed by orthogonal
similarity transformations into the form [A]=[Q][H][Q]^{T} where
[H] is upper (or lower) hessenberg matrix
[Q] is orthogonal/unitary

Hessenberg.Factory<N extends Number>  
LDL<N extends Number> 
LDL: [A] = [L][D][L]^{H} (or [R]^{H}[D][R])

LDL.Factory<N extends Number>  
LDU<N extends Number> 
LDU: [A] = [L][D][U] ( [P1][L][D][U][P2] )

LU<N extends Number> 
LU: [A] = [L][U]

LU.Factory<N extends Number>  
MatrixDecomposition<N extends Number> 
Notation used to describe the various matrix decompositions:
[A] could be any matrix.

MatrixDecomposition.Determinant<N extends Number>  
MatrixDecomposition.EconomySize<N extends Number> 
Several matrix decompositions can be expressed "economy sized"  some rows or columns of the decomposed
matrix parts are not needed for the most releveant use cases, and can therefore be left out.

MatrixDecomposition.Factory<D extends MatrixDecomposition<?>>  
MatrixDecomposition.Hermitian<N extends Number> 
Some matrix decompositions are only available with hermitian (symmetric) matrices or different
decomposition algorithms could be used depending on if the matrix is hemitian or not.

MatrixDecomposition.Ordered<N extends Number>  
MatrixDecomposition.RankRevealing<N extends Number> 
A rankrevealing matrix decomposition of a matrix [A] is a decomposition that is, or can be transformed
to be, on the form [A]=[X][D][Y]^{T} where:
[X] and [Y] are square and well conditioned.
[D] is diagonal with nonnegative and nonincreasing values on the diagonal.

MatrixDecomposition.Solver<N extends Number>  
MatrixDecomposition.Values<N extends Number> 
Eigenvalue and Singular Value decompositions can calculate the "values" only, and the resulting
matrices and arrays can have their elements sorted (descending) or not.

QR<N extends Number> 
QR: [A] = [Q][R] Decomposes [this] into [Q] and [R] where:
[Q] is an orthogonal matrix (orthonormal columns).

QR.Factory<N extends Number>  
Schur<N extends Number>  Deprecated
v43 Use Eigenvalue instead

SingularValue<N extends Number> 
Singular Value: [A] = [Q1][D][Q2]^{T} Decomposes [this] into [Q1], [D] and [Q2] where:
[Q1] is an orthogonal matrix.

SingularValue.Factory<N extends Number>  
Tridiagonal<N extends Number> 
Tridiagonal: [A] = [Q][D][Q]^{H} Any square symmetric (hermitian) matrix [A] can be factorized by
similarity transformations into the form, [A]=[Q][D][Q]^{1} where [Q] is an orthogonal (unitary)
matrix and [D] is a real symmetric tridiagonal matrix.

Tridiagonal.Factory<N extends Number> 
Class  Description 

Eigenvalue.Eigenpair  
EvD1D  
EvD2D  
HermitianEvD<N extends Number> 
Eigenvalues and eigenvectors of a real matrix.

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