title: python scipy eigs : return eigenvector after maximum number of iterations whatever the convergence tolerance
body: <p>I would like to get eigenvectors of a sparse symmetric matrix with the best precision affordable in a given time.<br>
  Currently I use the following with <code>scipy.sparse.eigsh</code>:</p>
  
  <pre><code>evals, evecs = eigsh(MyMatrix, 2,which='LM' ,tol=1.e-15, maxiter=1000000)
  </code></pre>
  
  <p>If it does not converge to <code>tol</code> precision by <code>maxiter</code> iterations, it raises an <code>ArpackNoConvergence</code> error which contains the eigenvectors/values that have converged, but not the ones that did not. Yet, I prefer to have the vector with precision <code>1.e-14</code> instead of <code>1.e-15</code> rather than no vector at all. Is there a way to force returning the eigenvectors that have not converged yet (perhaps with another library) ?<br>
  Like in Matlab, where the <code>eigs</code> function returns the eigenvectors anyway, with just an additional Warning if the desired precision is not reached.</p>
  
  <p>Thanks !</p>