Uses of Package
cern.colt.matrix.tdouble.algo.decomposition

Packages that use cern.colt.matrix.tdouble.algo.decomposition

Package	Description
cern.colt.matrix.tdouble.algo	Linear Algebraic matrix computations operating on DoubleMatrix2D and DoubleMatrix1D.
cern.colt.matrix.tdouble.algo.decomposition	Martrix decompositions.

Classes in cern.colt.matrix.tdouble.algo.decomposition used by cern.colt.matrix.tdouble.algo

Class and Description
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.
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.

Classes in cern.colt.matrix.tdouble.algo.decomposition used by cern.colt.matrix.tdouble.algo.decomposition

Class and Description
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