#350 Optimization with GEKKO in Python

# Modeling language for large-scale linear, quadratic, nonlinear, 
# and mixed-integer programming.
# Supported solvers: APOPT, BPOPT, IPOPT, SNOPT, and MINOS
#pip install gekko in conda window
from gekko import GEKKO
# Define environment
prob = GEKKO(remote=False)
# Design variables
x = prob.Var(lb=0,ub=None,integer=True)
y = prob.Var(lb=0,ub=None)
# Objective function
prob.Obj(-(2*x+5*y))
# Add constraints to the environment
prob.Equation(5*x+3*y<=10)
prob.Equation(2*x+7*y<=9)
# Solve the problem (1 = MINLP solver, 2,3: Other Solvers)
prob.options.SOLVER=1  
prob.solve(disp=False) 
print('Results:')
print('x = ' + str(x.value))
print('y = ' + str(y.value))
print('Optimal value of obj = ' + str(-prob.options.objfcnval))
#Results:
#x = [1.0]
#y = [1.0]
#Optimal value of obj = 7.0