#351 Optimization using SciPy

from scipy.optimize import linprog
# set up cost list with cost function coefficient values
c = [-2,-3]
# set up constraint coefficient matrix A
A_ub = [[1,1],[2,1]]
# constraint list for upper bounds (less than or equal constraints)
b_ub =[10,15]
# in addition, i need to prepare a bounds tuple for each
# optimization variable and summarize them a list
x1_bounds = (0,None)
x2_bounds = (0,None)
# now I use SciPy.optimize.linprog to model and solve the problem at hand
model_linear = linprog(c=c,A_ub=A_ub,b_ub=b_ub,bounds=[x1_bounds,x2_bounds])
# output model solution
print(str(model_linear))
#Results:
# message: Optimization terminated successfully. (HiGHS Status 7: Optimal)
# success: True
# status: 0
# fun: -30.0
# x: [ 0.000e+00  1.000e+01]