I\'ve just check the simple linear programming problem with scipy.optimize.linprog:
1*x[1] + 2x[2] -> max
1*x[1] + 0*x[2] <= 5
0*x[1] + 1*x[2] <= 5
optimize.linprog
always minimizes your target function. If you want to maximize instead, you can use that max(f(x)) == -min(-f(x))
from scipy import optimize
optimize.linprog(
c = [-1, -2],
A_ub=[[1, 1]],
b_ub=[6],
bounds=(1, 5),
method='simplex'
)
This will give you your expected result, with the value -f(x) = -11.0
slack: array([ 0., 4., 0., 4., 0.])
message: 'Optimization terminated successfully.'
nit: 3
x: array([ 1., 5.])
status: 0
success: True
fun: -11.0