| Package | Description |
|---|---|
| cern.colt.matrix.tfloat.algo |
Linear Algebraic matrix computations operating on
FloatMatrix2D
and FloatMatrix1D. |
| Class and Description |
|---|
| 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.
|
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