| Interface | Description |
|---|---|
| BidiagonalDecomposition<T extends Matrix> |
Computes a matrix decomposition such that:
A = U*B*VT where A is m by n, U is orthogonal and m by m, B is an m by n bidiagonal matrix, V is orthogonal and n by n. |
| CholeskyDecomposition<MatrixType extends Matrix> |
Cholesky decomposition for
DenseMatrix64F. |
| CholeskyLDLDecomposition<MatrixType extends Matrix> |
Cholesky LDLT decomposition for
DenseMatrix64F. |
| DecompositionInterface<T extends Matrix> |
An interface for performing matrix decompositions on a
DenseMatrix64F. |
| EigenDecomposition<MatrixType extends Matrix> |
This is a generic interface for computing the eigenvalues and eigenvectors of a matrix.
|
| LUDecomposition<T extends Matrix> |
LU Decomposition refactors the original matrix such that: PT*L*U = A
where P is a pivot matrix, L is a lower triangular matrix, U is an upper triangular matrix and A is
the original matrix.
|
| QRDecomposition<T extends Matrix> |
QR decompositions decompose a rectangular matrix 'A' such that 'A=QR'.
|
| QRPDecomposition<T extends Matrix> |
Similar to
QRDecomposition but it can handle the rank deficient case by
performing column pivots during the decomposition. |
| SingularValueDecomposition<T extends Matrix> |
This is an abstract class for computing the singular value decomposition (SVD) of a matrix, which is defined
as: A = U * W * V T
where A is m by n, and U and V are orthogonal matrices, and W is a diagonal matrix. |
| TridiagonalSimilarDecomposition<MatrixType extends Matrix> |
Finds the decomposition of a matrix in the form of:
A = O*T*OT where A is a symmetric m by m matrix, O is an orthogonal matrix, and T is a tridiagonal matrix. |