Our algorithm follows a similar two-phase approach, but we emphasize the differences, in particular with XGandalf. as follows: In XGandalf, the first phase involves sampling vectors and optimizing them using Gradient Descent (GD). However, GD is inherently sequential, which hinders parallelism and, in the case of XGandalf, requires a large number of iterations, and employs a set of hand-crafted functions to avoid being trapped in local minima. The final tuning phase also relies on GD. Furthermore, using GD is not inherently robust, as it does not effectively account for outliers; instead, XGandalf combines several heuristics to mitigate their influence.