研究人员开发了一种方法，用于学习交替方向乘子法（ADMM）中松弛参数的在线更新。该方法旨在通过为特定问题类别调整参数来提高ADMM的性能，ADMM是一种用于结构化凸优化的技术。与标准方法相比，学习到的策略在基准二次规划的迭代次数和执行时间方面均显示出改进。 AI
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排序理由 关于优化技术的学术论文。
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研究人员开发了用于ADMM的学习策略以提高优化性能
报道来源 [3]
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Learning Over-Relaxation Policies for ADMM with Convergence Guarantees
arXiv:2604.26932v1 Announce Type: cross Abstract: The Alternating Direction Method of Multipliers (ADMM) is a widely used method for structured convex optimization, and its practical performance depends strongly on the choice of penalty and relaxation parameters. Motivated by set…
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Learning Over-Relaxation Policies for ADMM with Convergence Guarantees
The Alternating Direction Method of Multipliers (ADMM) is a widely used method for structured convex optimization, and its practical performance depends strongly on the choice of penalty and relaxation parameters. Motivated by settings such as Model Predictive Control (MPC), wher…
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Learning Over-Relaxation Policies for ADMM with Convergence Guarantees
The Alternating Direction Method of Multipliers (ADMM) is a widely used method for structured convex optimization, and its practical performance depends strongly on the choice of penalty and relaxation parameters. Motivated by settings such as Model Predictive Control (MPC), wher…