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
1*x[1] + 0*x[2] >= 1
0*x[1] + 1*x[2] >= 1
1*x[1] + 1*x[2] <= 6
And got the very strange result, I expected that x[1] will be 1 and x[2] will be 5, but:
>>> print optimize.linprog([1, 2], A_ub=[[1, 1]], b_ub=[6], bounds=(1, 5), method='simplex')
status: 0
slack: array([ 4., 4., 4., 0., 0.])
success: True
fun: 3.0
x: array([ 1., 1.])
message: 'Optimization terminated successfully.'
nit: 2
Can anyone explain, why I got this strange result?
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
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