def F_to_K(self, F):
        """
        Compute agent 2's best cost-minimizing response K, given F.

        Parameters
        ----------
        F : array_like(float, ndim=2)
            A k x n array

        Returns
        -------
        K : array_like(float, ndim=2)
            Agent's best cost minimizing response for a given F
        P : array_like(float, ndim=2)
            The value function for a given F

        """
        Q2 = self.beta * self.theta
        R2 = - self.R - dot(F.T, dot(self.Q, F))
        A2 = dot(self.B, F) - self.A
        B2 = self.C
        lq = LQ(Q2, R2, A2, B2, beta=self.beta)
        neg_P, neg_K, d = lq.stationary_values()

        return -neg_K, -neg_P
dataset/ETHPy150Open QuantEcon/QuantEcon.py/quantecon/robustlq.py/RBLQ.F_to_K/swapped operands of `-`