I currently use Eigen for Polynomial fitting using L2 norm (least squares fitting), like this:

Code: Select all

Eigen::MatrixXd A(points.size(), degree+1);
for (int i=0; i<points.size(); i++)
{
    double xik=1;
    for (int k=0; k<=degree; k++)
    {
        A(i,k)=xik;
        xik*=points[i]->x;
    }
}
Eigen::JacobiSVD<Eigen::MatrixXd> svd(A, Eigen::ComputeFullU | Eigen::ComputeFullV);

Eigen::VectorXd b(points.size());
for (int i=0; i<points.size(); i++)
    b[i]=points[i]->y;
poly_coeff=svd.solve(b);

Is there a similar, or even any way to do L1-norm fitting of polynomial to points using Eigen?

You could try iteratively reweighed least squares,
which is a loop around your current code to alternatively least-square fit, and recalculate the weights.

There's no mathemathical guarantee for convergence, however, so you'd have to test it to see
how well it works for your problem.
http://en.wikipedia.org/wiki/Iteratively_reweighted_least_squares#Lp_norm_linear_regression

I solved it using lpsolve library, see here:
http://stackoverflow.com/questions/1706 ... ng-l1-norm