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

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

cern.colt.matrix.tfloat.algo

Linear Algebraic matrix computations operating on FloatMatrix2D and FloatMatrix1D.

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

DenseFloatCholeskyDecomposition
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.

DenseFloatEigenvalueDecomposition
Eigenvalues and eigenvectors of a real matrix A.

DenseFloatLUDecomposition
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.

DenseFloatQRDecomposition
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.

DenseFloatSingularValueDecomposition
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'.

SparseFloatCholeskyDecomposition
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.

SparseFloatLUDecomposition
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

SparseFloatQRDecomposition
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.