Update redundant_facet_removal_backup.py

dingo/backup/redundant_facet_removal_backup.py

@@ -304,89 +304,3 @@ def fast_remove_redundant_facets(lb, ub, S, c, opt_percentage=100):
 
 
 
-def fast_inner_ball(A, b):
-    """A Python function to compute the maximum inscribed ball in the given polytope using gurobi LP solver
-    Returns the optimal solution for the following linear program:
-    max r, subject to,
-    a_ix + r||a_i|| <= b, i=1,...,n
-
-    Keyword arguments:
-    A -- an mxn matrix that contains the normal vectors of the facets of the polytope row-wise
-    b -- a m-dimensional vector
-    """
-
-    extra_column = []
-
-    m = A.shape[0]
-    n = A.shape[1]
-
-    for i in range(A.shape[0]):
-        entry = np.linalg.norm(
-            A[
-                i,
-            ]
-        )
-        extra_column.append(entry)
-
-    column = np.asarray(extra_column)
-    A_expand = np.c_[A, column]
-
-    with gp.Env(empty=True) as env:
-        env.setParam("OutputFlag", 0)
-        env.start()
-
-        d = A_expand.shape[1]
-
-        with gp.Model(env=env) as model:
-
-            # Create variable
-            x = model.addMVar(
-                shape=d,
-                vtype=GRB.CONTINUOUS,
-                name="x",
-                lb=-GRB.INFINITY,
-                ub=GRB.INFINITY,
-            )
-            model.update()
-
-            # Make A_full_dim sparse
-            A_expand_sparse = sp.csr_matrix(A_expand.astype("float"))
-
-            # Add constraints
-            model.addMConstr(A_expand_sparse, x, "<", b, name="c")
-            model.update()
-
-            # Set the ith row of the A matrix as the objective function
-            a = np.ones((1, n + 1))
-            azero = np.zeros((1, n))
-            a[:, :-1] = azero
-            objective_function = a[0]
-
-            # Set the objective function in the model
-            model.setMObjective(
-                None, objective_function, 0.0, None, None, x, GRB.MAXIMIZE
-            )
-            model.update()
-
-            # Optimize model
-            model.optimize()
-
-            # Get the solution returned
-            varss = model.getVars()
-
-            # Get the center point and the radius of max ball from the solution of LP; its last element is the radius
-            point = []
-            for i in range(len(varss)):
-                if i == len(varss) - 1:
-                    r = varss[i].x
-                else:
-                    value = varss[i].x
-                    point.append(value)
-
-            # And check whether the computed radius is negative
-            if r < 0:
-                print(
-                    "The radius calculated has negative value. The polytope is infeasible or something went wrong with the solver"
-                )
-            else:
-                return point, r