Eigen linear algebra solvers seem slow

问题

I want to solve a linear algebraic equation Ax = b using Eigen solvers. In my case, A is a complex sparse matrix(26410*26410), b is a real vector (26410*1).

I use mex file in MATLAB to map the sparse matrix A and vector b to Eigen accepted format. The reason why I use Eigen solver is to hope it would be faster than solving directly in MATLAB using x = A\b.

However, after tried LDLT, SparseLU, CG and BiCGSTAB, I found the results are not very satisfying:

LDLT takes 1.462s with norm(A*x - b)/norm(b) = 331; SparseLU takes 37.994s with 1.5193e-4; BiCGSTAB takes 95.217s with 4.5977e-4; On the contrast, directly use x = A\b in MATLAB consumes 13.992s with norm of the error 2.606e-5.

I know it is a little stupid and also time consuming to map the sparse matrix A and vector b in MATLAB workspace to Eigen. But I am wondering whether the results I got are the best results which Eigen can give? Anyone can give me some pointers? Should I try some other linear equation solvers? Thanks a lot in advance! The following is the main part of codes.

void mexFunction(int nlhs, mxArray *plhs[], int nrhs, const mxArray *prhs[]) {

    //input vars

    //temp var
    size_t nrows;

    //output vars
    //double *x;

    //GetData
    /* check inputs 
    ...*/

    //"mxArray2SCM" is a sub-function for map the complex sparse matrix in Eigen
    SpCMat A = mxArray2SCM(prhs[0]);
    //SpMat A = mxArray2SM(prhs[0]);

    //"mxArray2ECV" is a sub-function for map the real vector in Eigen
    Eigen::VectorXcd b = mxArray2ECV(prhs[1]);
    //Eigen::VectorXd b = mxArray2EV(prhs[1]);   
    nrows = b.size();
    //Computation
    Eigen::VectorXcd x(nrows);
    //SparseLU<SparseMatrix<CD> > solver;
    BiCGSTAB<SparseMatrix<CD>,IncompleteLUT<CD> > BiCG;
    //BiCG.preconditioner().setDroptol(0.001);
    BiCG.compute(A);
    if(BiCG.info()!=Success){
        //decomposition failed
        return;
    }
    x = BiCG.solve(b);

    //Output results
    plhs[0] = ECV2mxArray(x);   
}