title: Singularity for inverse matrix
body: <p>As data I get a matrix A but in my algorithm I need to work on its inverse. What I do is:</p>
  
  <pre><code>C = inv(A) + B;
  </code></pre>
  
  <p>Then in another line I update A. In the next cycles I also need (updated) A inverse, again for this algorithm. And so on. In the later cycles I get this:</p>
  
  <pre><code>Matrix is close to singular or badly scaled. Results may be inaccurate. RCOND = 1.425117e-019
  </code></pre>
  
  <p>or this:   </p>
  
  <pre><code>Warning: Matrix is singular to working precision.
  </code></pre>
  
  <p>or this:</p>
  
  <pre><code>Warning: Matrix is singular, close to singular or badly scaled. Results may be inaccurate. RCOND = NaN.
  </code></pre>
  
  <p>Can you help me how to avoid such singularity? Matrix is squared always.</p>