See: Description
Interface | Description |
---|---|
ARPACK |
ARPACK is a collection of Fortran77 subroutines designed to
solve large scale eigenvalue problems.
|
DoubleConsumer |
Double precision matrix element stream consumer.
|
FloatConsumer |
Single precision matrix element stream consumer.
|
LinearSolver |
The interface of the solver of linear system.
|
PageRank |
PageRank is a link analysis algorithm and it assigns a numerical weighting
to each element of a hyperlinked set of documents, such as the World Wide
Web, with the purpose of "measuring" its relative importance within the
set.
|
Preconditioner |
The preconditioner matrix in the biconjugate gradient method.
|
Class | 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.
|
BandMatrix.Cholesky |
The Cholesky decomposition of a symmetric, positive-definite matrix.
|
BandMatrix.LU |
The LU decomposition.
|
BiconjugateGradient |
The biconjugate gradient method is an algorithm to
solve systems of linear equations.
|
DMatrix |
Double precision matrix base class.
|
FloatBandMatrix |
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.
|
FloatBandMatrix.Cholesky |
The Cholesky decomposition of a symmetric, positive-definite matrix.
|
FloatBandMatrix.LU |
The LU decomposition.
|
FloatMatrix | |
FloatMatrix.Cholesky |
The Cholesky decomposition of a symmetric, positive-definite matrix.
|
FloatMatrix.EVD |
Eigenvalue decomposition.
|
FloatMatrix.LU |
The LU decomposition.
|
FloatMatrix.QR |
The QR decomposition.
|
FloatMatrix.SVD |
Singular Value Decomposition.
|
FloatSparseMatrix |
A sparse matrix is a matrix populated primarily with zeros.
|
FloatSymmMatrix |
They symmetric matrix in packed storage.
|
FloatSymmMatrix.BunchKaufman |
The LU decomposition.
|
FloatSymmMatrix.Cholesky |
The Cholesky decomposition of a symmetric, positive-definite matrix.
|
IMatrix<T> |
An abstract interface of matrix.
|
Lanczos |
The Lanczos algorithm is a direct algorithm devised by Cornelius Lanczos
that is an adaptation of power methods to find the most useful eigenvalues
and eigenvectors of an nth order linear system with a limited
number of operations, m, where m is much smaller than n.
|
Matrix | |
Matrix.Cholesky |
The Cholesky decomposition of a symmetric, positive-definite matrix.
|
Matrix.EVD |
Eigenvalue decomposition.
|
Matrix.LU |
The LU decomposition.
|
Matrix.QR |
The QR decomposition.
|
Matrix.SVD |
Singular Value Decomposition.
|
PowerIteration |
The power iteration (also known as power method) is an eigenvalue algorithm
that will produce the greatest (in absolute value) eigenvalue and a nonzero
vector the corresponding eigenvector.
|
SMatrix |
Single precision matrix base class.
|
SparseMatrix |
A sparse matrix is a matrix populated primarily with zeros.
|
SymmMatrix |
They symmetric matrix in packed storage.
|
SymmMatrix.BunchKaufman |
The LU decomposition.
|
SymmMatrix.Cholesky |
The Cholesky decomposition of a symmetric, positive-definite matrix.
|
Enum | Description |
---|---|
ARPACK.AsymmOption |
Which eigenvalues of asymmetric matrix to compute.
|
ARPACK.SymmOption |
Which eigenvalues of symmetric matrix to compute.
|
One of most important matrix operations is the matrix vector multiplication, which is the only operation needed in many iterative matrix algorithms, e.g. biconjugate gradient method for solving linear equations and power iteration and Lanczos algorithm for eigen decomposition, which are usually very efficient for very large and sparse matrices.