Uses of Package
smile.math.matrix

Packages that use smile.math.matrix

Package	Description
smile.math	Basic mathematical functions, complex, differentiable function interfaces, random number generators, unconstrained optimization, and raw data type (int and double) array lists, etc.
smile.math.matrix	Matrix interface, dense and sparse (band or irregular) matrix encapsulation classes, LU, QR, Cholesky, SVD and eigen decompositions, etc.

Classes in smile.math.matrix used by smile.math

Class and Description
EigenValueDecomposition Eigen decomposition of a real matrix.
IMatrix An abstract interface of matrix.
SingularValueDecomposition Singular Value Decomposition.

Classes in smile.math.matrix used by smile.math.matrix

Class and Description
BandMatrix A band matrix is a sparse matrix, whose non-zero entries are confined to a diagonal band, comprising the main diagonal and zero or more diagonals on either side.
CholeskyDecomposition Cholesky decomposition is a decomposition of a symmetric, positive-definite matrix into a lower triangular matrix L and the transpose of the lower triangular matrix such that A = L*L'.
EigenValueDecomposition Eigen decomposition of a real matrix.
IMatrix An abstract interface of matrix.
LUDecomposition For an m-by-n matrix A with m ≥ n, the LU decomposition is an m-by-n unit lower triangular matrix L, an n-by-n upper triangular matrix U, and a permutation vector piv of length m so that A(piv,:) = L*U.
Matrix A matrix is a rectangular array of numbers.
QRDecomposition For an m-by-n matrix A with m ≥ n, the QR decomposition is an m-by-n orthogonal matrix Q and an n-by-n upper triangular matrix R such that A = Q*R.
SingularValueDecomposition Singular Value Decomposition.
SparseMatrix A sparse matrix is a matrix populated primarily with zeros.