Research paper unifies CoCoA and ADMM optimization algorithms

A new research paper explores the relationship between two families of distributed optimization algorithms, CoCoA and ADMM. By unifying them through a primal-dual perspective, the study reveals that certain ADMM variants can perform comparably to or better than CoCoA for ridge-regularized empirical risk minimization problems. The unified view also provides a new primal-dual gap stopping criterion for consensus ADMM and a consistent convergence analysis for ADMM-type methods. AI

影响 Provides a unified theoretical framework for distributed optimization, potentially improving efficiency in training large-scale machine learning models.

排序理由 The cluster contains an academic paper detailing a new theoretical framework for optimization algorithms. [lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv stat.ML 阅读 →

• CoCoA
• Empirical Risk Minimization
• Runxiong Wu

• 论文
• 其他

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