Uses of Package
org.ejml.interfaces.decomposition
Package
Description
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ClassDescriptionCholesky decomposition.Implementation of
CholeskyDecomposition
for 32-bit floats.Implementation ofCholeskyDecomposition
for 64-bit 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*OT
where A is a symmetric m by m matrix, O is an orthogonal matrix, and T is a tridiagonal matrix.Implementation ofTridiagonalSimilarDecomposition
for 32-bit floatsImplementation ofTridiagonalSimilarDecomposition
for 64-bit floats -
ClassDescriptionAn interface for performing matrix decompositions.QR decompositions decompose a rectangular matrix 'A' such that 'A=QR'.
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ClassDescriptionImplementation of
CholeskyDecomposition
for 32-bit floats.Implementation ofCholeskyDecomposition
for 64-bit floats. -
ClassDescriptionQR decompositions decompose a rectangular matrix 'A' such that 'A=QR'.
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ClassDescriptionImplementation of
EigenDecomposition
for 32-bit floatsImplementation ofEigenDecomposition
for 64-bit floatsImplementation ofSingularValueDecomposition
for 32-bit floats.Implementation ofSingularValueDecomposition
for 64-bit floats. -
ClassDescriptionCholesky decomposition.Implementation of
CholeskyDecomposition
for 32-bit floats.Implementation ofCholeskyDecomposition
for 64-bit 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*OT
where A is a symmetric m by m matrix, O is an orthogonal matrix, and T is a tridiagonal matrix.Implementation ofTridiagonalSimilarDecomposition
for 32-bit floatsImplementation ofTridiagonalSimilarDecomposition
for 64-bit floats -
ClassDescriptionAn interface for performing matrix decompositions.LU Decomposition refactors the original matrix such that:
PT*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 32-bit numbersImplementation ofLUDecomposition
for 64-bit numbers -
ClassDescriptionAn interface for performing matrix decompositions.QR decompositions decompose a rectangular matrix 'A' such that 'A=QR'.
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ClassDescriptionComputes a matrix decomposition such that:
A = U*B*VT
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 32-bit floatsImplementation ofBidiagonalDecomposition
for 64-bit floatsAn interface for performing matrix decompositions. -
ClassDescriptionCholesky decomposition.Implementation of
CholeskyDecomposition
for 32-bit floats.Implementation ofCholeskyDecomposition
for 64-bit floats.Cholesky LDLT decomposition.Implementation ofCholeskyDecomposition
for 32-bit floats.Implementation ofCholeskyDecomposition
for 64-bit 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 32-bit floatsImplementation ofEigenDecomposition
for 64-bit floatsImplementation ofTridiagonalSimilarDecomposition
for 32-bit floatsImplementation ofTridiagonalSimilarDecomposition
for 64-bit floats -
ClassDescriptionAn interface for performing matrix decompositions.Finds the decomposition of a matrix in the form of:
A = O*T*OT
where A is a symmetric m by m matrix, O is an orthogonal matrix, and T is a tridiagonal matrix.Implementation ofTridiagonalSimilarDecomposition
for 32-bit floatsImplementation ofTridiagonalSimilarDecomposition
for 64-bit floats -
ClassDescriptionAn interface for performing matrix decompositions.LU Decomposition refactors the original matrix such that:
PT*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 32-bit numbersImplementation ofLUDecomposition
for 64-bit 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 32-bit floatsImplementation ofQRPDecomposition
for 64-bit floats -
ClassDescriptionImplementation of
BidiagonalDecomposition
for 32-bit floatsImplementation ofBidiagonalDecomposition
for 64-bit 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 32-bit floats.Implementation ofSingularValueDecomposition
for 64-bit floats. -
ClassDescriptionImplementation of
CholeskyDecomposition
for 32-bit floats.Implementation ofCholeskyDecomposition
for 64-bit floats.Implementation ofCholeskyDecomposition
for 32-bit floats.Implementation ofCholeskyDecomposition
for 64-bit floats.An interface for performing matrix decompositions.Implementation ofEigenDecomposition
for 32-bit floatsImplementation ofEigenDecomposition
for 64-bit floatsImplementation ofLUDecomposition
for 32-bit numbersImplementation ofLUDecomposition
for 64-bit numbersQR decompositions decompose a rectangular matrix 'A' such that 'A=QR'.Implementation ofQRPDecomposition
for 32-bit floatsImplementation ofQRPDecomposition
for 64-bit 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 32-bit floats.Implementation ofSingularValueDecomposition
for 64-bit floats.Implementation ofTridiagonalSimilarDecomposition
for 32-bit floatsImplementation ofTridiagonalSimilarDecomposition
for 64-bit floats -
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ClassDescriptionImplementation of
CholeskyDecomposition
for 32-bit floats.Implementation ofCholeskyDecomposition
for 64-bit floats.Implementation ofCholeskyDecomposition
for 32-bit floats.Implementation ofCholeskyDecomposition
for 64-bit floats. -
ClassDescriptionQR decompositions decompose a rectangular matrix 'A' such that 'A=QR'.Implementation of
QRPDecomposition
for 32-bit floatsImplementation ofQRPDecomposition
for 64-bit 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 32-bit floats.Implementation ofSingularValueDecomposition
for 64-bit floats. -
ClassDescriptionComputes a matrix decomposition such that:
A = U*B*VT
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 LDLT 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:
PT*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*OT
where A is a symmetric m by m matrix, O is an orthogonal matrix, and T is a tridiagonal matrix. -
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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 32-bit floats.Implementation ofCholeskySparseDecomposition
for 64-bit 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:
PT*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 32-bit numbersImplementation ofLUSparseDecomposition
for 64-bit numbers -
ClassDescriptionAn interface for performing matrix decompositions.Decomposition for sparse matrices.QR decompositions decompose a rectangular matrix 'A' such that 'A=QR'.Sparse
QRDecomposition
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ClassDescriptionImplementation of
CholeskySparseDecomposition
for 32-bit floats.Implementation ofCholeskySparseDecomposition
for 64-bit floats.Implementation ofLUSparseDecomposition
for 32-bit numbersImplementation ofLUSparseDecomposition
for 64-bit numbersSparseQRDecomposition
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