title: eigendecomposition error tolerance
body: <p>I have a very simple question. It is related to the computational tolerance error. </p>
  
  <p>Let me do (see at the end) the eigendecomposition of a matrix A in eigenvector V and diagonal eigenvalues D, and build it again by multiplication V^-1*D*V.</p>
  
  <p>The obtained value is far from being A, the error is quite big. </p>
  
  <p>I would like to know if I am using incorrect functions to do this task or, at least, how can I reduce this error. Thank you in advance</p>
  
  <pre><code>in[1]:import numpy
        from scipy import linalg
        A=matrix([[16,-9,0],[-9,20,-11],[0,-11,11]])
        D,V=linalg.eig(A)
        D=diagflat(D)
        matrix(linalg.inv(V))*matrix(D)*matrix(V)
  
  
  out[1]:matrix([[ 15.52275377,   9.37603361,   0.79257097],  
         [9.37603361,  21.12538282, -10.23535271],  
         [0.79257097, -10.23535271,  10.35186341]])
  </code></pre>