Scipy minimize with multiple linear constraints

I'm having an issue trying to implement an NLP using scipy, given a dynamic vector x and a static utility vector u, where I'm interested in minimizing a utility function of the form:

utility_vector = np.array([0.10, 0.08, 0.05, 0.075, 0.32,                            0.21, 0.18, 0.05, 0.03, 0.12])x0 = np.zeros((10, )) groups = [[0, 1, 2, 3], [4, 5], [6, 7, 8, 9]]z_group = [0.25, 0.55, 0.2]def opt_func(x, u, target):    utility = (x * u).sum()    return (utility - target)**2

Alongside a number of sub-total linear constraints of the form x[selection].sum() = Z1 and a total constraint of x.sum() = 1.0. However, when applying these constraints via scipy.minimize, my response vector does not seem to satisfy the sub-total linear constraints being specified.

cons = []# total linear constraint cons.append({'type': 'eq', 'fun': lambda x: 1 - x.sum()})# sub-total linear constraintsfor idx, select in enumerate(groups):    cons.append({'type': 'eq', 'fun': lambda x: z_group[idx] - x[select].sum()})bnds = tuple((0, None) for i in range(10))res = minimize(fun=opt_func,                x0=x0,                method='trust-constr',                bounds=bnds,                constraints=tuple(cons),                args=(utility_vector,                      0.16),               tol=1e-4) print(res)print(f'\nTotal allocation sum {res.x.sum()}')# outputfor idx, select in enumerate(groups):    print(f'{select} fields difference {z_group[idx] - res.x[select].sum()}')

Any help or insight into how I may go about addressing this would be greatly appreciated.