#### 39.2.3 Dogleg

This is Powell’s dogleg method, which finds an approximate solution to the trust region subproblem, by restricting its search to a piecewise linear “dogleg” path, composed of the origin, the Cauchy point which represents the model minimizer along the steepest descent direction, and the Gauss-Newton point, which is the overall minimizer of the unconstrained model. The Gauss-Newton step is calculated by solving

    J_k \delta_gn = -f_k

which is the main computational task for each iteration, but only needs to be performed once per iteration. If the Gauss-Newton point is inside the trust region, it is selected as the step. If it is outside, the method then calculates the Cauchy point, which is located along the gradient direction. If the Cauchy point is also outside the trust region, the method assumes that it is still far from the minimum and so proceeds along the gradient direction, truncating the step at the trust region boundary. If the Cauchy point is inside the trust region, with the Gauss-Newton point outside, the method uses a dogleg step, which is a linear combination of the gradient direction and the Gauss-Newton direction, stopping at the trust region boundary.