template<typename _MatrixType>
class Eigen::PartialPivLU< _MatrixType >
LU decomposition of a matrix with partial pivoting, and related features.
- Template Parameters
-
_MatrixType |
the type of the matrix of which we are computing the LU decomposition |
This class represents a LU decomposition of a square invertible matrix, with partial pivoting: the matrix A is decomposed as A = PLU where L is unit-lower-triangular, U is upper-triangular, and P is a permutation matrix.
Typically, partial pivoting LU decomposition is only considered numerically stable for square invertible matrices. Thus LAPACK's dgesv and dgesvx require the matrix to be square and invertible. The present class does the same. It will assert that the matrix is square, but it won't (actually it can't) check that the matrix is invertible: it is your task to check that you only use this decomposition on invertible matrices.
The guaranteed safe alternative, working for all matrices, is the full pivoting LU decomposition, provided by class FullPivLU.
This is not a rank-revealing LU decomposition. Many features are intentionally absent from this class, such as rank computation. If you need these features, use class FullPivLU.
This LU decomposition is suitable to invert invertible matrices. It is what MatrixBase::inverse() uses in the general case. On the other hand, it is not suitable to determine whether a given matrix is invertible.
The data of the LU decomposition can be directly accessed through the methods matrixLU(), permutationP().
This class supports the inplace decomposition mechanism.
- See also
-
MatrixBase::partialPivLu(), MatrixBase::determinant(), MatrixBase::inverse(), MatrixBase::computeInverse(), class FullPivLU
template<typename _MatrixType >
template<typename Rhs >
This method returns the solution x to the equation Ax=b, where A is the matrix of which *this is the LU decomposition.
- Parameters
-
b |
the right-hand-side of the equation to solve. Can be a vector or a matrix, the only requirement in order for the equation to make sense is that b.rows()==A.rows(), where A is the matrix of which *this is the LU decomposition. |
- Returns
- the solution.
Example:
MatrixXd A = MatrixXd::Random(3,3);
MatrixXd B = MatrixXd::Random(3,2);
cout << "Here is the invertible matrix A:" << endl << A << endl;
cout << "Here is the matrix B:" << endl << B << endl;
MatrixXd X = A.lu().solve(B);
cout << "Here is the (unique) solution X to the equation AX=B:" << endl << X << endl;
cout << "Relative error: " << (A*X-B).norm() / B.norm() << endl;
Output:
Here is the invertible matrix A:
0.68 0.597 -0.33
-0.211 0.823 0.536
0.566 -0.605 -0.444
Here is the matrix B:
0.108 -0.27
-0.0452 0.0268
0.258 0.904
Here is the (unique) solution X to the equation AX=B:
0.609 2.68
-0.231 -1.57
0.51 3.51
Relative error: 3.28e-16
Since this PartialPivLU class assumes anyway that the matrix A is invertible, the solution theoretically exists and is unique regardless of b.
- See also
- TriangularView::solve(), inverse(), computeInverse()