title: How do I add limiting conditions when using GpyOpt?
body: <p>Currently I try to minimize the function and get optimized parameters using GPyOpt.</p>
  
  <pre><code>import GPy
  import GPyOpt
  from math import log
  def f(x):
      x0,x1,x2,x3,x4,x5 = x[:,0],x[:,1],x[:,2],x[:,3],x[:,4],x[:,5],
      f0 = 0.2 * log(x0)
      f1 = 0.3 * log(x1)
      f2 = 0.4 * log(x2)
      f3 = 0.2 * log(x3)
      f4 = 0.5 * log(x4)
      f5 = 0.2 * log(x5)
      return -(f0 + f1 + f2 + f3 + f4 + f5) 
  
  bounds = [
      {'name': 'x0', 'type': 'discrete', 'domain': (1,1000000)},
      {'name': 'x1', 'type': 'discrete', 'domain': (1,1000000)},
      {'name': 'x2', 'type': 'discrete', 'domain': (1,1000000)},
      {'name': 'x3', 'type': 'discrete', 'domain': (1,1000000)},
      {'name': 'x4', 'type': 'discrete', 'domain': (1,1000000)},
      {'name': 'x5', 'type': 'discrete', 'domain': (1,1000000)}
  ]
  
  myBopt = GPyOpt.methods.BayesianOptimization(f=f, domain=bounds)
  myBopt.run_optimization(max_iter=100)
  print(myBopt.x_opt) 
  print(myBopt.fx_opt) 
  </code></pre>
  
  <p>I want to add limiting conditions to this function.
  Here is an example.</p>
  
  <pre><code>x0 + x1 + x2 + x3 + x4 + x5 == 100000000
  </code></pre>
  
  <p>How should I modify this code?</p>