Bregman optimization algorithms can get stuck near false stationary points

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.

RANK_REASON This is a research paper detailing theoretical findings about optimization algorithms. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

• Bregman proximal-type algorithms
• Jiajin Li
• machine learning

• paper
• other

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