Uses of Package
org.ejml.interfaces.decomposition
Package
Description


ClassDescriptionCholesky decomposition.Implementation of
CholeskyDecomposition
for 32bit floats.Implementation ofCholeskyDecomposition
for 64bit floats.An interface for performing matrix decompositions. 
ClassDescriptionAn interface for performing matrix decompositions.Finds the decomposition of a matrix in the form of:
A = O*T*O^{T}
where A is a symmetric m by m matrix, O is an orthogonal matrix, and T is a tridiagonal matrix.Implementation ofTridiagonalSimilarDecomposition
for 32bit floatsImplementation ofTridiagonalSimilarDecomposition
for 64bit floats 
ClassDescriptionAn interface for performing matrix decompositions.QR decompositions decompose a rectangular matrix 'A' such that 'A=QR'.

ClassDescriptionImplementation of
CholeskyDecomposition
for 32bit floats.Implementation ofCholeskyDecomposition
for 64bit floats. 
ClassDescriptionQR decompositions decompose a rectangular matrix 'A' such that 'A=QR'.

ClassDescriptionImplementation of
EigenDecomposition
for 32bit floatsImplementation ofEigenDecomposition
for 64bit floatsImplementation ofSingularValueDecomposition
for 32bit floats.Implementation ofSingularValueDecomposition
for 64bit floats. 
ClassDescriptionCholesky decomposition.Implementation of
CholeskyDecomposition
for 32bit floats.Implementation ofCholeskyDecomposition
for 64bit floats.An interface for performing matrix decompositions. 
ClassDescriptionAn interface for performing matrix decompositions.Finds the decomposition of a matrix in the form of:
A = O*T*O^{T}
where A is a symmetric m by m matrix, O is an orthogonal matrix, and T is a tridiagonal matrix.Implementation ofTridiagonalSimilarDecomposition
for 32bit floatsImplementation ofTridiagonalSimilarDecomposition
for 64bit floats 
ClassDescriptionAn interface for performing matrix decompositions.LU Decomposition refactors the original matrix such that:
P^{T}*L*U = A where P is a pivot matrix, L is a lower triangular matrix, U is an upper triangular matrix and A is the original matrix.Implementation ofLUDecomposition
for 32bit numbersImplementation ofLUDecomposition
for 64bit numbers 
ClassDescriptionAn interface for performing matrix decompositions.QR decompositions decompose a rectangular matrix 'A' such that 'A=QR'.


ClassDescriptionComputes a matrix decomposition such that:
A = U*B*V^{T}
where A is m by n, U is orthogonal and m by m, B is an m by n bidiagonal matrix, V is orthogonal and n by n.Implementation ofBidiagonalDecomposition
for 32bit floatsImplementation ofBidiagonalDecomposition
for 64bit floatsAn interface for performing matrix decompositions. 
ClassDescriptionCholesky decomposition.Implementation of
CholeskyDecomposition
for 32bit floats.Implementation ofCholeskyDecomposition
for 64bit floats.Cholesky LDL^{T} decomposition.Implementation ofCholeskyDecomposition
for 32bit floats.Implementation ofCholeskyDecomposition
for 64bit floats.An interface for performing matrix decompositions. 
ClassDescriptionAn interface for performing matrix decompositions.This is a generic interface for computing the eigenvalues and eigenvectors of a matrix.Implementation of
EigenDecomposition
for 32bit floatsImplementation ofEigenDecomposition
for 64bit floatsImplementation ofTridiagonalSimilarDecomposition
for 32bit floatsImplementation ofTridiagonalSimilarDecomposition
for 64bit floats 
ClassDescriptionAn interface for performing matrix decompositions.Finds the decomposition of a matrix in the form of:
A = O*T*O^{T}
where A is a symmetric m by m matrix, O is an orthogonal matrix, and T is a tridiagonal matrix.Implementation ofTridiagonalSimilarDecomposition
for 32bit floatsImplementation ofTridiagonalSimilarDecomposition
for 64bit floats 
ClassDescriptionAn interface for performing matrix decompositions.LU Decomposition refactors the original matrix such that:
P^{T}*L*U = A where P is a pivot matrix, L is a lower triangular matrix, U is an upper triangular matrix and A is the original matrix.Implementation ofLUDecomposition
for 32bit numbersImplementation ofLUDecomposition
for 64bit numbers 
ClassDescriptionAn interface for performing matrix decompositions.QR decompositions decompose a rectangular matrix 'A' such that 'A=QR'.Similar to
QRDecomposition
but it can handle the rank deficient case by performing column pivots during the decomposition.Implementation ofQRPDecomposition
for 32bit floatsImplementation ofQRPDecomposition
for 64bit floats 
ClassDescriptionImplementation of
BidiagonalDecomposition
for 32bit floatsImplementation ofBidiagonalDecomposition
for 64bit floatsAn interface for performing matrix decompositions.This is an abstract class for computing the singular value decomposition (SVD) of a matrix, which is defined as:
A = U * W * V ^{T} where A is m by n, and U and V are orthogonal matrices, and W is a diagonal matrix.Implementation ofSingularValueDecomposition
for 32bit floats.Implementation ofSingularValueDecomposition
for 64bit floats. 
ClassDescriptionImplementation of
CholeskyDecomposition
for 32bit floats.Implementation ofCholeskyDecomposition
for 64bit floats.Implementation ofCholeskyDecomposition
for 32bit floats.Implementation ofCholeskyDecomposition
for 64bit floats.An interface for performing matrix decompositions.Implementation ofEigenDecomposition
for 32bit floatsImplementation ofEigenDecomposition
for 64bit floatsImplementation ofLUDecomposition
for 32bit numbersImplementation ofLUDecomposition
for 64bit numbersQR decompositions decompose a rectangular matrix 'A' such that 'A=QR'.Implementation ofQRPDecomposition
for 32bit floatsImplementation ofQRPDecomposition
for 64bit floatsThis is an abstract class for computing the singular value decomposition (SVD) of a matrix, which is defined as:
A = U * W * V ^{T} where A is m by n, and U and V are orthogonal matrices, and W is a diagonal matrix.Implementation ofSingularValueDecomposition
for 32bit floats.Implementation ofSingularValueDecomposition
for 64bit floats.Implementation ofTridiagonalSimilarDecomposition
for 32bit floatsImplementation ofTridiagonalSimilarDecomposition
for 64bit floats 

ClassDescriptionImplementation of
CholeskyDecomposition
for 32bit floats.Implementation ofCholeskyDecomposition
for 64bit floats.Implementation ofCholeskyDecomposition
for 32bit floats.Implementation ofCholeskyDecomposition
for 64bit floats. 
ClassDescriptionQR decompositions decompose a rectangular matrix 'A' such that 'A=QR'.Implementation of
QRPDecomposition
for 32bit floatsImplementation ofQRPDecomposition
for 64bit floats 
ClassDescriptionThis is an abstract class for computing the singular value decomposition (SVD) of a matrix, which is defined as:
A = U * W * V ^{T} where A is m by n, and U and V are orthogonal matrices, and W is a diagonal matrix.Implementation ofSingularValueDecomposition
for 32bit floats.Implementation ofSingularValueDecomposition
for 64bit floats. 
ClassDescriptionComputes a matrix decomposition such that:
A = U*B*V^{T}
where A is m by n, U is orthogonal and m by m, B is an m by n bidiagonal matrix, V is orthogonal and n by n.Cholesky decomposition.Cholesky LDL^{T} decomposition.An interface for performing matrix decompositions.Decomposition for sparse matrices.This is a generic interface for computing the eigenvalues and eigenvectors of a matrix.LU Decomposition refactors the original matrix such that:
P^{T}*L*U = A where P is a pivot matrix, L is a lower triangular matrix, U is an upper triangular matrix and A is the original matrix.QR decompositions decompose a rectangular matrix 'A' such that 'A=QR'.Similar toQRDecomposition
but it can handle the rank deficient case by performing column pivots during the decomposition.This is an abstract class for computing the singular value decomposition (SVD) of a matrix, which is defined as:
A = U * W * V ^{T} where A is m by n, and U and V are orthogonal matrices, and W is a diagonal matrix.Finds the decomposition of a matrix in the form of:
A = O*T*O^{T}
where A is a symmetric m by m matrix, O is an orthogonal matrix, and T is a tridiagonal matrix. 

ClassDescriptionThis is a generic interface for computing the eigenvalues and eigenvectors of a matrix.This is an abstract class for computing the singular value decomposition (SVD) of a matrix, which is defined as:
A = U * W * V ^{T} where A is m by n, and U and V are orthogonal matrices, and W is a diagonal matrix. 
ClassDescriptionCholesky decomposition.Implementation of
CholeskySparseDecomposition
for 32bit floats.Implementation ofCholeskySparseDecomposition
for 64bit floats.An interface for performing matrix decompositions.Decomposition for sparse matrices. 
ClassDescriptionAn interface for performing matrix decompositions.Decomposition for sparse matrices.LU Decomposition refactors the original matrix such that:
P^{T}*L*U = A where P is a pivot matrix, L is a lower triangular matrix, U is an upper triangular matrix and A is the original matrix.Implementation ofLUSparseDecomposition
for 32bit numbersImplementation ofLUSparseDecomposition
for 64bit numbers 
ClassDescriptionAn interface for performing matrix decompositions.Decomposition for sparse matrices.QR decompositions decompose a rectangular matrix 'A' such that 'A=QR'.Sparse
QRDecomposition

ClassDescriptionImplementation of
CholeskySparseDecomposition
for 32bit floats.Implementation ofCholeskySparseDecomposition
for 64bit floats.Implementation ofLUSparseDecomposition
for 32bit numbersImplementation ofLUSparseDecomposition
for 64bit numbersSparseQRDecomposition


