Solving constrained nonlinear minimization with scipy in python
An attempt to solve a simple one-variable nonlinear minimization problem.
from scipy.optimize import minimize
import math
alpha = 0.05
waiting = 50
mean_period = 50
neighborhood_size = 5
def my_func(w):
return -(2/(w+1) + alpha*math.floor(waiting/mean_period))*(1-(2/(w+1) + alpha*math.floor(waiting/mean_period)))**(neighborhood_size-1)
print minimize(my_func, mean_period, bounds=(2,200))
which gives me
ValueError: length of x0 != length of bounds
Am I entering this incorrectly? How do I format it?
And if I remove the borders I get:
status: 2
success: False
njev: 19
nfev: 69
hess_inv: array([[1]])
fun: array([-0.04072531])
x: array([50])
message: 'Desired error not necessarily achieved due to precision loss.'
jac: array([-1386838.30676792])
The function looks like which , and so I need constraints to constrain the solution to the local maximum I'm interested in.
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1 answer
It should be:
print minimize(my_func, mean_period, bounds=((2,200),))
status: 0
success: True
nfev: 57
fun: array([-0.08191999])
x: array([ 12.34003932])
message: 'CONVERGENCE: NORM_OF_PROJECTED_GRADIENT_<=_PGTOL'
jac: array([ 2.17187379e-06])
nit: 4
For each parameter, you must provide an estimate, so here we need to pass tuple
in which contains only one tuple
(2,200)
, to minimize()
.
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