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首页> 外文会议>Asilomar Conference on Signals, Systems and Computers >Penalty-based multitask distributed adaptation over networks with constraints
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Penalty-based multitask distributed adaptation over networks with constraints

机译：带有约束的基于惩罚的多任务分布式适应网络

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摘要

Multitask distributed optimization over networks enables the agents to cooperate locally to estimate multiple related parameter vectors. In this work, we consider multitask estimation problems over mean-square-error (MSE) networks where each agent is interested in estimating its own parameter vector, also called task, and where the tasks are related according to a set of linear equality constraints. We assume that each agent possesses its own cost and that the set of constraints is distributed among the agents. In order to solve the multitask problem, a cooperative algorithm based on penalty method is derived. Some results on its stability and convergence properties are also provided. Simulations are conducted to illustrate the theoretical results and show the efficiency of the strategy.
机译：网络上的多任务分布式优化使代理可以在本地协作以估计多个相关参数向量。在这项工作中，我们考虑了均方误差（MSE）网络上的多任务估计问题，其中每个代理都希望估计自己的参数矢量（也称为任务），并且根据一组线性相等约束将任务相关联。我们假设每个代理商都有自己的成本，并且约束集在代理商之间分配。为了解决多任务问题，提出了一种基于惩罚算法的协同算法。还提供了一些有关其稳定性和收敛性的结果。进行仿真以说明理论结果并显示该策略的效率。

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