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This research explores the PHC Algorithm, aimed at minimizing positional differences between landmarks by optimizing motor parameter vectors. It leverages Gradient Descent for iterative updates, emphasizing proper gradient calculation and learning rate tuning. Detailed insights into the Jacobian matrix and tensor-based gradient representations are provided for function vectorization. The implementation steps involve derivative computation, iterative descent, and ensuring convergence. The...