Regularized Mixed Newton Method for Global Optimization of Real Analytic Functions

The regularized mixed Newton method (RMNM) applied to real analytic functions in complex space exhibits superior global convergence properties compared to traditional methods by leveraging the repulsive nature of saddle points in complex space, effectively converging to global minima while outperforming in specific machine learning tasks.