Setting bounds and constraints for scipy dual_annealing

Question

I am trying to run simulated annealing to solve the following problem:

Minimize: Z = 5x + 4y

4x + y ≥ 40,

2x + 3y ≥ 90,

x, y ≥ 0

However I faced two problems:

1. adding constriants
2. setting the bounds

Scipy's dual_annealing() doesn't support any constraints as parameters (unlike functions such as minimize()). While setting the bounds to [0, inf). I get some errors. My code and the errors are shown below.

def obj(v):
    x, y = v
    return 5*x + 4*y

# define range for input
r_min, r_max = 0, np.inf

result = dual_annealing(objective, bounds)

If I use bounds as bounds = [[r_min, r_max], [r_min, r_max]] I get this error:

   640         np.isnan(lower)) or np.any(np.isnan(upper))):
--> 641     raise ValueError('Some bounds values are inf values or nan values')
    642 # Checking that bounds are consistent
    643 if not np.all(lower < upper):

ValueError: Some bounds values are inf values or nan values

And if I use bounds = Bounds(lb=r_min), I get this error:

--> 639 if (np.any(np.isinf(lower)) or np.any(np.isinf(upper)) or np.any(
    640         np.isnan(lower)) or np.any(np.isnan(upper))):
    641     raise ValueError('Some bounds values are inf values or nan values')
    642 # Checking that bounds are consistent

TypeError: ufunc 'isinf' not supported for the input types, and the inputs could not be safely coerced to any supported types according to the casting rule ''safe''
2

Most of the examples online use fixed bounds and unconstrained problems. How can I add infinity bounds and constraints?