[Python] Python linprog to maximise objective function

It has been a while since I have done this so I am a bit rusty, but equation is:

max t(C)*x
s.t. Ax <=b


And I have my A matrix of constraints which is (1448x1359) :

[[ 1. 1. 0. ..., 0. 0. 0.]

...,


[ 0. 0. 0. ..., 1. 1. 1.]]


Then I have my binding b (1448x1):

[ 1. 1. 7. ..., 2. 1. 2.]


And my objective function to be maximised which is a vector of ones (1359,1).

Now in other packages my maximised objective function is 841, however using linprog:

res = linprog(c=OBJ_N, A_ub=A, b_ub=b, options={"disp": True})


It optimised successfully to -0.0 so I wonder if I'm using the right command in python and have my constraints the right way around?

Edit: Ok that makes sense, it was trying to minimise. I have rewritten now (swapped c and b and transposed A to minimise).

# (max t(C)*x s.t. Ax <=b) = min t(b)*x s.t. ATy = c, y ≥ 0
# (i): minimise number of shops no bounds
ID = np.ones(len(w[0]))
print(ID)
print(ID.shape) #1359

At = A.transpose()

need_divest = (A.dot(ID)) - 1
print(need_divest)
print(need_divest.shape) #1448

res = linprog(c=need_divest, A_eq=At, b_eq=ID, options={"disp": True})
print(res)


However, I get "message: 'Optimzation failed. Unable to find a feasible starting point.'"

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