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Parallel Colt 0.9.4 | |||||||||
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Description
Class Summary | |
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DenseDoubleCholeskyDecomposition | For a symmetric, positive definite matrix A, the Cholesky decomposition is a lower triangular matrix L so that A = L*L'; If the matrix is not symmetric positive definite, the IllegalArgumentException is thrown. |
DenseDoubleEigenvalueDecomposition | Eigenvalues and eigenvectors of a real matrix A. |
DenseDoubleLUDecomposition | For an m x n matrix A with m >= n, the LU decomposition is an m x n unit lower triangular matrix L, an n x n upper triangular matrix U, and a permutation vector piv of length m so that A(piv,:) = L*U; If m < n, then L is m x m and U is m x n. |
DenseDoubleLUDecompositionQuick | A low level version of DenseDoubleLUDecomposition , avoiding
unnecessary memory allocation and copying. |
DenseDoubleQRDecomposition | For an m x n matrix A with m >= n, the QR decomposition is an m x n orthogonal matrix Q and an n x n upper triangular matrix R so that A = Q*R. |
DenseDoubleSingularValueDecomposition | For an m x n matrix A, the singular value decomposition is an m x m orthogonal matrix U, an m x n diagonal matrix S, and an n x n orthogonal matrix V so that A = U*S*V'. |
SparseDoubleCholeskyDecomposition | For a symmetric, positive definite matrix A, the Cholesky decomposition is a lower triangular matrix L so that A = L*L'; If the matrix is not symmetric positive definite, the IllegalArgumentException is thrown. |
SparseDoubleLUDecomposition | For a square matrix A, the LU decomposition is an unit lower triangular matrix L, an upper triangular matrix U, and a permutation vector piv so that A(piv,:) = L*U |
SparseDoubleQRDecomposition | For an m x n matrix A with m >= n, the QR decomposition is an m x n orthogonal matrix Q and an n x n upper triangular matrix R so that A = Q*R. |
Martrix decompositions.
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Parallel Colt 0.9.4 | |||||||||
PREV PACKAGE NEXT PACKAGE | FRAMES NO FRAMES |