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

Martrix decompositions.

See:
Description

Class Summary

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.

Package cern.colt.matrix.tdouble.algo.decomposition Description

Martrix decompositions.