Osqp Data Setting in python

Hi The OSQP community,

I am very new in using python osqp. I am trying to find absolute values of variables from linear equations such that the problem is as follows:
arg min 1Tt
Subject to Ax=b
-ti <= xi <= ti
To solve this problem, I have set the matrices and vectors in the following manner:

q = np.array([0,0,0,1,1,1])
b = np.array([4,6])
A1 = sparse.vstack([sparse.vstack([sparse.hstack([[[1,52,63],[6,1,24]], sparse.csc_matrix((m,n))]), sparse.vstack([sparse.hstack([sparse.eye(n), -sparse.eye(n)]), sparse.hstack([-sparse.eye(n), -sparse.eye(n)])])]),sparse.hstack([sparse.eye(n),sparse.csc_matrix((n,n))]), sparse.hstack([sparse.csc_matrix((n,n)), sparse.eye(n)])], format=‘csc’)

where A1 =
[A 0] [(m,n) (m,n)] , m= number of equations, n = number of variables
[I -I] [(n), (n)]
[-I -I] [(n), (n)]
[0 I] [(n,n), (n,n)]

l = np.hstack([b,-np.inf*np.ones(2*n),-np.inf*np.ones(n),np.zeros(n)])
u = np.hstack([b,np.zeros(2*n),np.inf*np.ones(n), np.inf*np.ones(n)])
prob.setup(None, q, A1, l, u, verbose=True)

From this setting, I am getting result as follows:
[-0.00030790136735674124, -0.23806677195847828, 0.25999648330179165, -6.954615701355441e-11, 0.23886031742885358, 0.26063126542158155]

Here, the first value should become positive in the fourth value like the other two. I am not understanding why the first value gives a negative result in the fourth one.

Can anyone help me in this regard? Do I need to change the l and u vectors or A?

Thank you very much in anticipation