Package org.ejml.interfaces.decomposition
package org.ejml.interfaces.decomposition
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InterfacesClassDescriptionBidiagonalDecomposition<T extends Matrix>Computes 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.BidiagonalDecomposition_F32<T extends Matrix>Implementation ofBidiagonalDecompositionfor 32-bit floatsBidiagonalDecomposition_F64<T extends Matrix>Implementation ofBidiagonalDecompositionfor 64-bit floatsCholeskyDecomposition<MatrixType extends Matrix>Cholesky decomposition.CholeskyDecomposition_F32<MatrixType extends Matrix>Implementation ofCholeskyDecompositionfor 32-bit floats.CholeskyDecomposition_F64<MatrixType extends Matrix>Implementation ofCholeskyDecompositionfor 64-bit floats.CholeskyLDLDecomposition<MatrixType extends Matrix>Cholesky LDLT decomposition.CholeskyLDLDecomposition_F32<MatrixType extends Matrix>Implementation ofCholeskyDecompositionfor 32-bit floats.CholeskyLDLDecomposition_F64<MatrixType extends Matrix>Implementation ofCholeskyDecompositionfor 64-bit floats.CholeskySparseDecomposition<MatrixType extends Matrix>CholeskySparseDecomposition_F32<MatrixType extends Matrix>Implementation ofCholeskySparseDecompositionfor 32-bit floats.CholeskySparseDecomposition_F64<MatrixType extends Matrix>Implementation ofCholeskySparseDecompositionfor 64-bit floats.DecompositionInterface<T extends Matrix>An interface for performing matrix decompositions.DecompositionSparseInterface<T extends Matrix>Decomposition for sparse matrices.EigenDecomposition<T extends Matrix>This is a generic interface for computing the eigenvalues and eigenvectors of a matrix.EigenDecomposition_F32<MatrixType extends Matrix>Implementation ofEigenDecompositionfor 32-bit floatsEigenDecomposition_F64<MatrixType extends Matrix>Implementation ofEigenDecompositionfor 64-bit floatsLUDecomposition<T extends 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.LUDecomposition_F32<T extends Matrix>Implementation ofLUDecompositionfor 32-bit numbersLUDecomposition_F64<T extends Matrix>Implementation ofLUDecompositionfor 64-bit numbersLUSparseDecomposition<MatrixType extends Matrix>LUSparseDecomposition_F32<T extends Matrix>Implementation ofLUSparseDecompositionfor 32-bit numbersLUSparseDecomposition_F64<T extends Matrix>Implementation ofLUSparseDecompositionfor 64-bit numbersQRDecomposition<T extends Matrix>QR decompositions decompose a rectangular matrix 'A' such that 'A=QR'.QRPDecomposition<T extends Matrix>Similar toQRDecompositionbut it can handle the rank deficient case by performing column pivots during the decomposition.QRPDecomposition_F32<T extends Matrix>Implementation ofQRPDecompositionfor 32-bit floatsQRPDecomposition_F64<T extends Matrix>Implementation ofQRPDecompositionfor 64-bit floatsQRSparseDecomposition<T extends Matrix>SparseQRDecompositionSingularValueDecomposition<T extends 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.SingularValueDecomposition_F32<T extends Matrix>Implementation ofSingularValueDecompositionfor 32-bit floats.SingularValueDecomposition_F64<T extends Matrix>Implementation ofSingularValueDecompositionfor 64-bit floats.TridiagonalSimilarDecomposition<MatrixType extends 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.TridiagonalSimilarDecomposition_F32<MatrixType extends Matrix>Implementation ofTridiagonalSimilarDecompositionfor 32-bit floatsTridiagonalSimilarDecomposition_F64<MatrixType extends Matrix>Implementation ofTridiagonalSimilarDecompositionfor 64-bit floats