title: How can I get integer solutions with scipy.optimize.linprog?
body: <p>When I solve the problem of Linear Programming, like in the following formula, <strong>I want the result of x all to be int type</strong></p>
  
  <p>Consider the following problem:</p>
  
  <p>Minimize: <code>f = -1*x[0] + 4*x[1]</code></p>
  
  <p>Subject to: </p>
  
  <pre><code>-3*x[0] + 1*x[1] &lt;= 6    
  1*x[0] + 2*x[1] &lt;= 4    
  x[1] &gt;= -3
  </code></pre>
  
  <p>where: <code>-inf &lt;= x[0] &lt;= inf</code></p>
  
  <p><em>next is the python coder</em></p>
  
  <pre><code>&gt;&gt;&gt; c = [-1, 4]
  &gt;&gt;&gt; A = [[-3, 1], [1, 2]]
  &gt;&gt;&gt; b = [6, 4]
  &gt;&gt;&gt; x0_bounds = (None, None)
  &gt;&gt;&gt; x1_bounds = (-3, None)
  &gt;&gt;&gt; res = linprog(c, A_ub=A, b_ub=b, bounds=(x0_bounds, x1_bounds),
  ...               options={"disp": True})
  &gt;&gt;&gt; print(res)
  Optimization terminated successfully.
  Current function value: -11.428571
  Iterations: 2
  status: 0
  success: True
  fun: -11.428571428571429
  x: array([-1.14285714,  2.57142857])
  message: 'Optimization terminated successfully.'
  nit: 2
  </code></pre>