How can I get integer solutions with scipy.optimize.linprog?

Question

When I solve the problem of Linear Programming, like in the following formula, I want the result of x all to be int type

Consider the following problem:

Minimize: f = -1*x[0] + 4*x[1]

Subject to:

-3*x[0] + 1*x[1] <= 6    
1*x[0] + 2*x[1] <= 4    
x[1] >= -3

where: -inf <= x[0] <= inf

next is the python coder

>>> c = [-1, 4]
>>> A = [[-3, 1], [1, 2]]
>>> b = [6, 4]
>>> x0_bounds = (None, None)
>>> x1_bounds = (-3, None)
>>> res = linprog(c, A_ub=A, b_ub=b, bounds=(x0_bounds, x1_bounds),
...               options={"disp": True})
>>> print(res)
Optimization terminated successfully.
Current function value: -11.428571
Iterations: 2
status: 0
success: True
fun: -11.428571428571429
x: array([-1.14285714,  2.57142857])
message: 'Optimization terminated successfully.'
nit: 2

Answer 1

From the docs:

method : str, optional Type of solver. At this time only ‘simplex’ is supported.

Simplex cannot handle integrality constraints so you cannot solve integer programming problems with scipy.optimize.linprog yet. You can try other libraries like PuLP, Pyomo or CVXOPT.