Scipy最小化约束功能

Question

I am solving the following optimization problem:

with this Python code:

from scipy.optimize import minimize
import math

def f(x):
    return math.log(x[0]**2 + 1) + x[1]**4 + x[0]*x[2]

x0 = [0, 0, 0]

cons=({'type': 'ineq',
       'fun': lambda x: x[0]**3 - x[1]**2 - 1},
      {'type': 'ineq',
       'fun': lambda x: x[0]},
      {'type': 'ineq',
       'fun': lambda x: x[2]})

res = minimize(f, x0, constraints=cons)
print res

I am getting an error

message: 'Inequality constraints incompatible'

What can cause this error?

Answer 1

The issue seems to be with your initial guess. If I change your starting values to

x0 = [1.0, 1.0, 1.0]

Then your code will execute fine (at least on my machine)

Python 3.5.1 (v3.5.1:37a07cee5969, Dec 6 2015, 01:54:25) [MSC v.1900 64 bit (AMD64)] on win32

 message: 'Optimization terminated successfully.'
    njev: 10
     jac: array([ 1.,  0.,  1.,  0.])
     fun: 0.6931471805582502
     nit: 10
  status: 0
       x: array([  1.00000000e+00,  -1.39724765e-06,   1.07686548e-14])
 success: True
    nfev: 51

Answer 2

Scipy's optimize module has lots of options. See the documentation or this tutorial . Since you didn't specify the method here, it will use Sequential Least SQuares Programming ( SLSQP ). Alternatively, you could use the Trust-Region Constrained Algorithm ( trust-const ).

For this problem, I found that trust-const seemed much more robust to starting values than SLSQP , handling starting values from [-2,-2,-2] to [10,10,10] , although negative initial values resulted in increased iterations, as you'd expect. Negative values below -2 exceeded the max iterations, although I suspect might still converge if you increased max iterations, although specifying negative values at all for x1 and x3 is kind of silly, of course, I just did it to get a sense of how robust it was to a range of starting values.

The specifications for SLSQP and trust-const are conceptually the same, but the syntax is a little different (in particular, note the use of NonlinearConstraint ).

from scipy.optimize import minimize, NonlinearConstraint, SR1

def f(x):
    return math.log(x[0]**2 + 1) + x[1]**4 + x[0]*x[2]

constr_func = lambda x: np.array( [ x[0]**3 - x[1]**2 - 1,
                                    x[0],
                                    x[2] ] )

x0=[0.,0.,0.]

nonlin_con = NonlinearConstraint( constr_func, 0., np.inf )

res = minimize( f, x0, method='trust-constr',
                jac='2-point', hess=SR1(),
                constraints = nonlin_con )

Here are the results, edited for conciseness:

    fun: 0.6931502233468916
message: '`gtol` termination condition is satisfied.'
      x: array([1.00000063e+00, 8.21427026e-09, 2.40956900e-06])

Note that the function value and x values are the same as in @CoryKramer's answer. The x array may look superficially different at first glance, but both answers round to [1, 0, 0] .