While raw power is a critical component, it is not the sole ingredient for TORO's success. From this marriage between ML methodologies and scientific computing, TORO benefits also from the use of principled optimization tech-niques. By employing robust optimization methods and closed-form updates at each algorithmic step, we can reject outliers in a principled way and achieve fast convergence of the estimates in a small number of iterations. This gives TORO a competitive advantage in terms of robustness and efficiency over existing methods that employ Gradient-descent updates for refinement of the estimates and heuristic rules for outlier removal.