title: Strange working of the python minimize procedure
body: <p>Working with 'scipy.optimize.minimize' I'm having strange using of the minimize procedure. Below is test code to show my results:</p>
  <pre><code>import numpy as np
  import pandas as pd
  from scipy.optimize import minimize
  
  def SES(good, a, h):
    print('good is : {}'.format(good))
    print('a is : {}'.format(a))
    print('h is : {}'.format(h))
    return 0
  
  good = [1,2,3,4,5,6]
  a = minimize(SES, x0 = good, args=(0.1, 1), method = 'L-BFGS-B', bounds = [[0.1, 0.3]]*len(good))
   
  </code></pre>
  <p>I'm expecting that SES function will print for 'good' parameter the values [1,2,3,4,5,6]. But I'm receiving the following output</p>
  <pre><code>good is : [0.3 0.3 0.3 0.3 0.3 0.3]
  a is : 0.1
  h is : 1
  </code></pre>
  <p>If I remove bounds  parameter then I receive output as I expect:</p>
  <pre><code>a = minimize(SES, x0 = good, args=(0.1, 1), method = 'L-BFGS-B')
  
  good is : [1. 2. 3. 4. 5. 6.]
  a is : 0.1
  h is : 1
  </code></pre>
  <p>Could you explain what I'm doing wrong...</p>