title: seeking convergence with optimize.fmin on scipy
body: <p>I have a function I want to minimize with <code>scipy.optimize.fmin</code>. Note that I force a <code>print</code> when my function is evaluated.</p>
  
  <p>My problem is, when I start the minimization, the value printed decreases untill it reaches a certain point (the value 46700222.800). There it continues to decrease by very small bites, e.g., 46700222.797,46700222.765,46700222.745,46700222.699,46700222.688,46700222.678
  So intuitively, I feel I have reached the minimum, since the length of each step are minus then 1. But the algorithm keeps running untill I get a "<code>Maximum number of function evaluations has been exceeded</code>" error.</p>
  
  <p>My question is: how can I force my algorithm to accept the value of the parameter when the function evaluation reaches a value from where it does not really evolve anymore (let say, I don't gain more than 1 after an iteration). I read that the options <code>ftol</code> could be used but it has absolutely no effect on my code. In fact, I don't even know what value to put for <code>ftol</code>. I tried everything from 0.00001 to 10000 and there is still no convergence.</p>