title: Python Scipy Optimization.minimize using SLSQP showing maximized results
body: <p>I am learning to optimize a multivariate constrained nonlinear problem with <code>scipy.optimize.minimize</code>,but received strange results.</p>
  
  <p>My problem:</p>
  
  <pre><code>minimize objfun
  
  objfun   x*y
  
  constraints 0&lt;=x&lt;=5,  0&lt;=y&lt;=5,  x+y==5
  </code></pre>
  
  <p>My code:</p>
  
  <pre><code>from scipy import optimize
  def func(x):
  
      return x[0]*x[1]
  
  bnds=((0,100),(0,5))
  
  cons=({'type':'eq','fun':lambda x:x[0]+x[1]-5})
  x0=[0,0]
  res= optimize.minimize(func,x0,method='SLSQP',bounds=bnds,constraints=cons)
  </code></pre>
  
  <p>Received results:</p>
  
  <p><code>status: 0
   success: True
      njev: 2
      nfev: 8
       fun: 6.2499999999999991
         x: array([ 2.5,  2.5])
   message: 'Optimization terminated successfully.'
       jac: array([ 2.5,  2.5,  0. ])
       nit: 2
  </code></p>
  
  <p>I am expecting the fun to be 0 or significantly close to 0  and x or y to be 0</p>