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?
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
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]
.
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