auglag.py 文件源码

def least_squares_multipliers(self, x):
        """Compute least-squares multipliers estimates."""
        al_model = self.model
        slack_model = self.model.model
        m = slack_model.m
        n = slack_model.n

        lim = max(2 * m, 2 * n)
        J = slack_model.jop(x)

        # Determine which bounds are active to remove appropriate columns of J
        on_bound = self.get_active_bounds(x,
                                          slack_model.Lvar,
                                          slack_model.Uvar)
        free_vars = np.setdiff1d(np.arange(n, dtype=np.int), on_bound)
        Jred = ReducedJacobian(J, np.arange(m, dtype=np.int),
                               free_vars)

        g = slack_model.grad(x) - J.T * al_model.pi

        lsqr = LSQRSolver(Jred.T)
        lsqr.solve(g[free_vars], itnlim=lim)
        if lsqr.optimal:
            al_model.pi += lsqr.x.copy()
        else:
            self.log.debug("lsqr failed to converge")
        return