import scipy
from scipy.optimize import minimize


lista=[0.002,0.006,0.003,0.02,0.008,0.006,0.05]

def assign(k):
    return [0.01 if 0<=x< k*0.005 else 0.02 if k*0.005 <= x <k*0.01
        else 0.05 if  k*0.01<=x<k*0.025 else 0.1 for x in lista]

def constraint(k):
    return sum(assign(k))-0.2

def fun(k):
    return k


k0=0
bnds=[(0,100)]
cons={'type':'eq','fun':constraint}
res=minimize(fun,k0,bounds=bnds,method='SLSQP',constraints=cons)