I am trying to solve linear equations in c++ using the Eigen library. For the simple equation Ax=b, I have a sparse matrix A that is n x n, and known values for b which is n x 1, but I need to find the values for x. I tried using

x = A.colPivHouseholderQr().solve(b);

but this solves it for a dense matrix and since my "n" value is quite large, I need to find a way to do this using an implementation that is specific to sparse matrices. I looked into using SparseLU but I've been having trouble trying figure out the syntax behind it. Eigen online says to write something like this :

SparseLU<SparseMatrix<scalar>, COLAMDOrdering<Index>> solver;
x = solver.solve(b);

but when I run this, I get errors saying that they don't know what scalar and Index are. Has anyone used SparseLU and know the syntax behind implementing it?

  • 6
    $\begingroup$ I'm not sure where you found this example. But instead of "scalar" you would need some floating point type, typically, double. Instead of "Index" you would typically use int. $\endgroup$ Commented Nov 19, 2015 at 1:47
  • 1
    $\begingroup$ This seems like a programming issue, rather than a computational science one, and it would be impossible to answer it without you saying what exactly the "errors" were. $\endgroup$
    – Kirill
    Commented Nov 19, 2015 at 2:12
  • $\begingroup$ Please post the errors as the solution might be as simple as taking Bill Greene's suggestion of using double's and int's instead of scalars and index $\endgroup$
    – James
    Commented Nov 19, 2015 at 6:46

1 Answer 1

Eigen::SparseLU<Eigen::SparseMatrix<double> > solverA;

if(solverA.info()!=Eigen::Success) {
    std::cout << "Oh: Very bad" <<"\n";
     std::cout<<"okay computed"<<"\n";
Eigen::VectorXd solnew = solverA.solve(b);

You don't need to mention the COLAMDOrdering but if you want you can declare the solver as :

Eigen::SparseLU<Eigen::SparseMatrix<double>, Eigen::COLAMDOrdering<int> > solver;

PS: More info about COLAMDOrdring can be found here.


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