论文标题
对亚空间约束的一阶分布式优化算法的鲁棒性和收敛分析
Robustness and Convergence Analysis of First-Order Distributed Optimization Algorithms over Subspace Constraints
论文作者
论文摘要
本文扩展了算法，从而消除了分散梯度下降的固定点偏置，以解决比子空间约束的分布式优化的更一般的问题。利用积分二次约束框架，我们根据最坏情况的鲁棒性和收敛速率分析了这些广义算法的性能。通过展示最初为共识设计的一种扩展算法如何解决多任务推理问题，可以证明我们框架的实用性。
This paper extends algorithms that remove the fixed point bias of decentralized gradient descent to solve the more general problem of distributed optimization over subspace constraints. Leveraging the integral quadratic constraint framework, we analyze the performance of these generalized algorithms in terms of worst-case robustness and convergence rate. The utility of our framework is demonstrated by showing how one of the extended algorithms, originally designed for consensus, is now able to solve a multitask inference problem.
