title: Maximize a function with many parameters (python)
body: <p>first, let me say that I lack experiences with scientific math or statistics - so this might be a very well-known problem, but I don't know where to start.</p>
  
  <p>I have a function <code>f(x1, x2, ..., xn)</code> where I need to guess the x'ses and find the highest value for <code>f</code>. The function has the following properties:</p>
  
  <ul>
  <li><p>the total number or parameters is usually around 40 to 60, so a brute-force approach is impossible.</p></li>
  <li><p>the possible values for each x range from 0.01 to 2.99</p></li>
  <li><p>the function is steady, meaning that a higher f value means that the guess for the parameters is better and vice versa.</p></li>
  </ul>
  
  <p>So far, I implemented a pretty basic method in python. It initially sets all parameters to 1, randomly guesses new values and checks if the f is higher than before. If not, roll back to the previous values.
  In a loop with 10,000 iterations this seems to work somehow, but the result is propably far from being perfect.</p>
  
  <p>Any suggestions on how to improve the search for the optimal parameters will be appreciated. When googling this issue things linke MCMC came up, but that seems like a very advanced method and I would need a lot of time to even understand the method. 
  Basic hints or concepts would help me more than elaborated methods and algorithms.</p>