Package cern.colt.matrix.tfloat.algo.decomposition

Martrix decompositions.

See:
Description

Class Summary

FloatCholeskyDecomposition	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 or positive definite, the constructor returns a partial decomposition and sets an internal flag that may be queried by the isSymmetricPositiveDefinite() method.
FloatEigenvalueDecomposition	Eigenvalues and eigenvectors of a real matrix A.
FloatLUDecomposition	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.
FloatLUDecompositionQuick	A low level version of FloatLUDecomposition, avoiding unnecessary memory allocation and copying.
FloatQRDecomposition	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.
FloatSingularValueDecomposition	For an m x n matrix A with m >= n, the singular value decomposition is an m x n orthogonal matrix U, an n x n diagonal matrix S, and an n x n orthogonal matrix V so that A = U*S*V'.
FloatSingularValueDecompositionDC	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'.

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

Martrix decompositions.