 Eigen  3.3.90 (mercurial changeset 560a7b0734d8) Inplace matrix decompositions

Starting from Eigen 3.3, the LU, Cholesky, and QR decompositions can operate inplace, that is, directly within the given input matrix. This feature is especially useful when dealing with huge matrices, and or when the available memory is very limited (embedded systems).

To this end, the respective decomposition class must be instantiated with a Ref<> matrix type, and the decomposition object must be constructed with the input matrix as argument. As an example, let us consider an inplace LU decomposition with partial pivoting.

Let's start with the basic inclusions, and declaration of a 2x2 matrix `A:`

codeoutput
#include <iostream>
#include <Eigen/Dense>
using namespace std;
using namespace Eigen;
int main()
{
MatrixXd A(2,2);
A << 2, -1, 1, 3;
cout << "Here is the input matrix A before decomposition:\n" << A << endl;
Here is the input matrix A before decomposition:
2 -1
1 3

No surprise here! Then, let's declare our inplace LU object `lu`, and check the content of the matrix `A:`

 PartialPivLU > lu(A); cout << "Here is the input matrix A after decomposition:\n" << A << endl; Here is the input matrix A after decomposition: 2 -10.5 3.5

Here, the `lu` object computes and stores the `L` and `U` factors within the memory held by the matrix `A`. The coefficients of `A` have thus been destroyed during the factorization, and replaced by the L and U factors as one can verify:

 cout << "Here is the matrix storing the L and U factors:\n" << lu.matrixLU() << endl; Here is the matrix storing the L and U factors: 2 -10.5 3.5

Then, one can use the `lu` object as usual, for instance to solve the Ax=b problem:

 MatrixXd A0(2,2); A0 << 2, -1, 1, 3; VectorXd b(2); b << 1, 2; VectorXd x = lu.solve(b); cout << "Residual: " << (A0 * x - b).norm() << endl; Residual: 0

Here, since the content of the original matrix `A` has been lost, we had to declared a new matrix `A0` to verify the result.

Since the memory is shared between `A` and `lu`, modifying the matrix `A` will make `lu` invalid. This can easily be verified by modifying the content of `A` and trying to solve the initial problem again:

 A << 3, 4, -2, 1; x = lu.solve(b); cout << "Residual: " << (A0 * x - b).norm() << endl; Residual: 15.8114

Note that there is no shared pointer under the hood, it is the responsibility of the user to keep the input matrix `A` in life as long as `lu` is living.

If one wants to update the factorization with the modified A, one has to call the compute method as usual:

 A0 = A; // save A lu.compute(A); x = lu.solve(b); cout << "Residual: " << (A0 * x - b).norm() << endl; Residual: 0

Note that calling compute does not change the memory which is referenced by the `lu` object. Therefore, if the compute method is called with another matrix `A1` different than `A`, then the content of `A1` won't be modified. This is still the content of `A` that will be used to store the L and U factors of the matrix `A1`. This can easily be verified as follows:

 MatrixXd A1(2,2); A1 << 5,-2,3,4; lu.compute(A1); cout << "Here is the input matrix A1 after decomposition:\n" << A1 << endl; Here is the input matrix A1 after decomposition: 5 -2 3 4

The matrix `A1` is unchanged, and one can thus solve A1*x=b, and directly check the residual without any copy of `A1:`

 x = lu.solve(b); cout << "Residual: " << (A1 * x - b).norm() << endl; Residual: 2.48253e-16

Here is the list of matrix decompositions supporting this inplace mechanism: