Spurious Stationarity and Hardness Results for Bregman Proximal-Type Algorithms
Researchers have identified a significant issue with Bregman proximal-type algorithms, commonly used in machine learning for optimization. These algorithms can become trapped near points that appear stationary but are not, a phenomenon termed "spurious stationary points." This can lead to misleading convergence signals, even in convex problems, because the algorithms may exhibit arbitrarily slow decreases in objective value. The findings suggest a critical blind spot in these methods, necessitating new theoretical frameworks and algorithmic safeguards for reliable convergence. AI
IMPACT Identifies a potential flaw in optimization methods used in ML, suggesting a need for new safeguards.