title: Alglib: solving A * x = b in a least squares sense
body: <p>I have a somewhat complicated algorithm that requires the fitting of a quadric to a set of points. This quadric is given by its parametrization <code>(u, v, f(u,v))</code>, where <code>f(u,v) = au^2+bv^2+cuv+du+ev+f</code>. 
  The coefficients of the <code>f(u,v)</code> function need to be found since I have a set of exactly 6 constraints this function should obey. The problem is that this set of constraints, although yielding a problem like <code>A*x = b</code>, is not completely well behaved to guarantee a unique solution.</p>
  
  <p>Thus, to cut it short, I'd like to use alglib's facilities to somehow either determine <code>A</code>'s pseudoinverse or directly find the best fit for the <code>x</code> vector. </p>
  
  <p>Apart from computing the SVD, is there a more direct algorithm implemented in this library that can solve a system in a least squares sense (again, apart from the SVD or from using the naive <code>inv(transpose(A)*A)*transpose(A)*b</code> formula for general least squares problems where A is not a square matrix?</p>