Ask linprog

You will wrap scipy.optimize.linprog in three small functions, so that you can check any problem of this chapter in one line.

The idea. linprog minimises $c_1x + c_2y$ subject to $A\mathbf{x} \le b$ and $x, y \ge 0$. To maximise, give it the negative of the objective and negate the answer. A rule with $\ge$ is turned round by multiplying its row and its limit by $-1$. The status code is 0 for solved, 2 for no feasible solution and 3 for unbounded.

Your tasks.

1. Finish maximise(c, A, b). Return (value, x, y), each rounded to $4$ decimals.
2. Finish minimise(c, A, b). Return (value, x, y), each rounded to $4$ decimals.
3. Finish outcome(c, A, b, maximise_it). Return the text "solved", "no feasible solution" or "unbounded".