It's not. The intersection constraints are not convex. You could use sequential convex programming to find a local optimum. edit: to back up my claim: the feas

It's not. The intersection constraints are not convex. You could use sequential convex programming to find a local optimum. edit: to back up my claim: the feasible set of a convex optimization problem must be convex. suppose you have two squares of side length 1 at positions x1 and x2. The following two solutions are feasible: x1a = (-2, 0), x2a = ( 2, 0) x1b = ( 2, 0), x2b = (-2, 0) but their midpoint is not: x1c = (x1a + x1b)/2 = (0, 0) x2c = (x2a + x2b)/2 = (0, 0) therefore the feasible set is not convex.