| Name | Description |
|---|---|
| CholeskyDecomposition | Cholesky Decomposition. For a symmetric, positive definite matrix A, the Cholesky decomposition is an 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 isSPD() method. This file based on a class from the public domain JAMA package. http://math.nist.gov/javanumerics/jama/ |
| EigenvalueDecomposition | |
| LUDecomposition | LU Decomposition. 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. If m less than n, then L is m-by-m and U is m-by-n. The LU decompostion with pivoting always exists, even if the matrix is singular, so the constructor will never fail. The primary use of the LU decomposition is in the solution of square systems of simultaneous linear equations. This will fail if isNonsingular() returns false. This file based on a class from the public domain JAMA package. http://math.nist.gov/javanumerics/jama/ |
| QRDecomposition | QR Decomposition. 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 so that A = Q*R. The QR decompostion always exists, even if the matrix does not have full rank, so the constructor will never fail. The primary use of the QR decomposition is in the least squares solution of nonsquare systems of simultaneous linear equations. This will fail if isFullRank() returns false. This file based on a class from the public domain JAMA package. http://math.nist.gov/javanumerics/jama/ |
| SingularValueDecomposition |