Uses of Interface
org.ojalgo.matrix.decomposition.MatrixDecomposition

Packages that use MatrixDecomposition

Package	Description
org.ojalgo.matrix.decomposition

Uses of MatrixDecomposition in org.ojalgo.matrix.decomposition

Classes in org.ojalgo.matrix.decomposition with type parameters of type MatrixDecomposition

Modifier and Type	Interface and Description
static interface	MatrixDecomposition.Factory<D extends MatrixDecomposition<?>>

Subinterfaces of MatrixDecomposition in org.ojalgo.matrix.decomposition

Modifier and Type	Interface and Description
interface	Bidiagonal<N extends Number> A general matrix [A] can be factorized by similarity transformations into the form [A]=[Q1][D][Q2] -1 where: [A] (m-by-n) is any, real or complex, matrix [D] (r-by-r) or (m-by-n) is, upper or lower, bidiagonal [Q1] (m-by-r) or (m-by-m) is orthogonal [Q2] (n-by-r) or (n-by-n) is orthogonal r = min(m,n)
interface	Cholesky<N extends Number> Cholesky: [A] = [L][L]H (or [R]H[R])
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).
interface	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
interface	LDL<N extends Number> LDL: [A] = [L][D][L]H (or [R]H[D][R])
interface	LDU<N extends Number> LDU: [A] = [L][D][U] ( [P1][L][D][U][P2] )
interface	LU<N extends Number> LU: [A] = [L][U]
static interface	MatrixDecomposition.Determinant<N extends Number>
static interface	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.
static interface	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.
static interface	MatrixDecomposition.Ordered<N extends Number>
static interface	MatrixDecomposition.RankRevealing<N extends Number> A rank-revealing 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 non-increasing values on the diagonal.
static interface	MatrixDecomposition.Solver<N extends Number>
static interface	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.
interface	QR<N extends Number> QR: [A] = [Q][R] Decomposes [this] into [Q] and [R] where: [Q] is an orthogonal matrix (orthonormal columns).
interface	Schur<N extends Number> Deprecated. v43 Use Eigenvalue instead
interface	SingularValue<N extends Number> Singular Value: [A] = [Q1][D][Q2]T Decomposes [this] into [Q1], [D] and [Q2] where: [Q1] is an orthogonal matrix.
interface	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.

Classes in org.ojalgo.matrix.decomposition that implement MatrixDecomposition

Modifier and Type	Class and Description
class	HermitianEvD<N extends Number> Eigenvalues and eigenvectors of a real matrix.