#### 39.2.4 Double Dogleg

This method is an improvement over the classical dogleg algorithm, which attempts to include information about the Gauss-Newton step while the iteration is still far from the minimum. When the Cauchy point is inside the trust region and the Gauss-Newton point is outside, the method computes a scaled Gauss-Newton point and then takes a dogleg step between the Cauchy point and the scaled Gauss-Newton point. The scaling is calculated to ensure that the reduction in the model m_k is about the same as the reduction provided by the Cauchy point.