I am learning to optimize a multivariate constrained nonlinear problem with scipy.optimize.minimize
,but received strange results.
My problem:
minimize objfun
objfun x*y
constraints 0<=x<=5, 0<=y<=5, x+y==5
My 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)
Received results:
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
I am expecting the fun to be 0 or significantly close to 0 and x or y to be 0