Uses of Package
cern.colt.matrix.linalg
Packages that use cern.colt.matrix.linalg
Package
Description
Matrix implementations; You normally need not look at this package, because all concrete classes implement the abstract interfaces of
cern.colt.matrix
, without subsetting or supersetting.Linear Algebraic matrix computations operating on
DoubleMatrix2D
and DoubleMatrix1D
.-
Classes in cern.colt.matrix.linalg used by cern.colt.matrix.implClassDescriptionLinear algebraic matrix operations operating on
DoubleMatrix2D
; concentrates most functionality of this package.Tests matrices for linear algebraic properties (equality, tridiagonality, symmetry, singularity, etc). -
Classes in cern.colt.matrix.linalg used by cern.colt.matrix.linalgClassDescriptionLinear algebraic matrix operations operating on
DoubleMatrix2D
; concentrates most functionality of this package.Subset of the BLAS (Basic Linear Algebra System); High quality "building block" routines for performing basic vector and matrix operations.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.Eigenvalues and eigenvectors of a real matrix A.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 invalid input: '<' n, then L is m x m and U is m x n.A low level version ofLUDecomposition
, avoiding unnecessary memory allocation and copying.Interface that represents a function object: a function that takes two arguments and returns a single value.Tests matrices for linear algebraic properties (equality, tridiagonality, symmetry, singularity, etc).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.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'.