Uses of Interface
smile.math.matrix.IMatrix

Packages that use IMatrix

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.

Uses of IMatrix in smile.math

Methods in smile.math with parameters of type IMatrix

Modifier and Type	Method and Description
static double	Math.eigen(IMatrix A, double[] v) Returns the largest eigen pair of matrix with the power iteration under the assumptions A has an eigenvalue that is strictly greater in magnitude than its other its other eigenvalues and the starting vector has a nonzero component in the direction of an eigenvector associated with the dominant eigenvalue.
static double	Math.eigen(IMatrix A, double[] v, double tol) Returns the largest eigen pair of matrix with the power iteration under the assumptions A has an eigenvalue that is strictly greater in magnitude than its other its other eigenvalues and the starting vector has a nonzero component in the direction of an eigenvector associated with the dominant eigenvalue.
static double	Math.solve(IMatrix A, double[] b, double[] x) Solves A * x = b by iterative biconjugate gradient method.
static double	Math.solve(IMatrix A, double[] b, double[] x, double tol) Solves A * x = b by iterative biconjugate gradient method.
static double	Math.solve(IMatrix A, double[] b, double[] x, double tol, int itol) Solves A * x = b by iterative biconjugate gradient method.
static double	Math.solve(IMatrix A, double[] b, double[] x, double tol, int itol, int maxIter) Solves A * x = b by iterative biconjugate gradient method.
static double	Math.solve(IMatrix A, IMatrix Ap, double[] b, double[] x) Solves A * x = b by iterative biconjugate gradient method.
static double	Math.solve(IMatrix A, IMatrix Ap, double[] b, double[] x, double tol) Solves A * x = b by iterative biconjugate gradient method.
static double	Math.solve(IMatrix A, IMatrix Ap, double[] b, double[] x, double tol, int itol) Solves A * x = b by iterative biconjugate gradient method.
static double	Math.solve(IMatrix A, IMatrix Ap, double[] b, double[] x, double tol, int itol, int maxIter) Solves A * x = b by iterative biconjugate gradient method.

Uses of IMatrix in smile.math.matrix

Classes in smile.math.matrix that implement IMatrix

Modifier and Type	Class and Description
class	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.
class	Matrix A matrix is a rectangular array of numbers.
class	SparseMatrix A sparse matrix is a matrix populated primarily with zeros.

Methods in smile.math.matrix that return IMatrix

Modifier and Type	Method and Description
IMatrix	IMatrix.set(int i, int j, double x) Set the entry value at row i and column j.

Methods in smile.math.matrix with parameters of type IMatrix

Modifier and Type	Method and Description
static SingularValueDecomposition	SingularValueDecomposition.decompose(IMatrix A, int k) Find k largest approximate singular triples of a matrix by the Lanczos algorithm.
static EigenValueDecomposition	EigenValueDecomposition.decompose(IMatrix A, int k) Find k largest approximate eigen pairs of a symmetric matrix by the Lanczos algorithm.
static SingularValueDecomposition	SingularValueDecomposition.decompose(IMatrix A, int k, double kappa) Find k largest approximate singular triples of a matrix by the Lanczos algorithm.
static EigenValueDecomposition	EigenValueDecomposition.decompose(IMatrix A, int k, double kappa) Find k largest approximate eigen pairs of a symmetric matrix by the Lanczos algorithm.
static double	EigenValueDecomposition.eigen(IMatrix A, double[] v) Returns the largest eigen pair of matrix with the power iteration under the assumptions A has an eigenvalue that is strictly greater in magnitude than its other eigenvalues and the starting vector has a nonzero component in the direction of an eigenvector associated with the dominant eigenvalue.
static double	EigenValueDecomposition.eigen(IMatrix A, double[] v, double tol) Returns the largest eigen pair of matrix with the power iteration under the assumptions A has an eigenvalue that is strictly greater in magnitude than its other eigenvalues and the starting vector has a nonzero component in the direction of an eigenvector associated with the dominant eigenvalue.
static double	EigenValueDecomposition.eigen(IMatrix A, double[] v, double p, double tol) Returns the largest eigen pair of matrix with the power iteration under the assumptions A has an eigenvalue that is strictly greater in magnitude than its other eigenvalues and the starting vector has a nonzero component in the direction of an eigenvector associated with the dominant eigenvalue.
static double	EigenValueDecomposition.eigen(IMatrix A, double[] v, double p, double tol, int maxIter) Returns the largest eigen pair of matrix with the power iteration under the assumptions A has an eigenvalue that is strictly greater in magnitude than its other eigenvalues and the starting vector has a nonzero component in the direction of an eigenvector associated with the dominant eigenvalue.
static double	EigenValueDecomposition.eigen(IMatrix A, double[] v, double tol, int maxIter) Returns the largest eigen pair of matrix with the power iteration under the assumptions A has an eigenvalue that is strictly greater in magnitude than its other eigenvalues and the starting vector has a nonzero component in the direction of an eigenvector associated with the dominant eigenvalue.
static double[]	EigenValueDecomposition.pagerank(IMatrix A) Calculate the page rank vector.
static double[]	EigenValueDecomposition.pagerank(IMatrix A, double[] v) Calculate the page rank vector.
static double[]	EigenValueDecomposition.pagerank(IMatrix A, double[] v, double damping, double tol, int maxIter) Calculate the page rank vector.