title: failing scipy.minimize for multiple constraints
body: <p>I would like to minimize the following function using scipy.minimize</p>
  
  <pre><code>def lower_bound(x, mu, r, sigma):
      mu_h = mu_hat(x, mu, r)
      sigma_h = sigma_hat(x, sigma)
      gauss = np.polynomial.hermite.hermgauss(10)
      return (1 + mu_h + math.sqrt(2) * sigma_h * min(gauss[1]))
  </code></pre>
  
  <p>all the involved function are tested and return values as expected. Now for setting up the minimization process, I defined</p>
  
  <pre><code>cons = ({"type": "ineq",
               "fun": mu_hat,
               "args": (mu, r)},
              {"type": "ineq",
               "fun": lambda x, sigma: -1.0*sigma_hat(x, sigma),
               "args": (sigma)},
              {"type": "ineq",
               "fun": lambda x: x},
              {"type": "ineq",
               "fun": lambda x: 1-np.dot(np.ones(x.size), x)})
  </code></pre>
  
  <p>as the constraints. When I run this code scipy.minimize gives me the following error message for the constraints:</p>
  
  <pre><code>File "/usr/local/lib/python3.5/dist-packages/scipy/optimize/slsqp.py", line 312, in &lt;listcomp&gt;
      mieq = sum(map(len, [atleast_1d(c['fun'](x, *c['args'])) for c in cons['ineq']]))
  TypeError: &lt;lambda&gt;() argument after * must be an iterable, not float
  </code></pre>
  
  <p>what is not correct with the defined constraints? </p>