title: Why eigs( 'lm') is much faster than eigs('sm')
body: <p>I use eigs to calculate the eigen vectors of sparse square matrices which are large (tens of thousands).
  What I want is the smallest set of eigen vectors.
  But </p>
  
  <pre><code>eigs(A, 10, 'sm')      % Note: A is the matrix
  </code></pre>
  
  <p>runs very slow.</p>
  
  <p>However, using eigs(A, 10, 'lm') gives me the answer relatively faster.
  And as I tried, replacing 10 with A_width in eigs(A, 10, 'lm') so that this includes all the eigen vectors, doesn't solve this problem, 'cause this make it the as slow as using 'sm'.</p>
  
  <p>So, I want to know why calculating the smallest vectors(using 'sm') is much slower than calculating the largest?</p>
  
  <p>BTW, if you have any idea about how to use eigs with 'sm' as fast as with 'lm', please tell me that.</p>