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.
  • Class
    Description
    Linear algebraic matrix operations operating on DoubleMatrix2D; concentrates most functionality of this package.
    Tests matrices for linear algebraic properties (equality, tridiagonality, symmetry, singularity, etc).
  • Class
    Description
    Linear 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 of LUDecomposition, 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'.