Control approach to distributed optimization
Control approach to distributed optimization
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DOI：
10.1109/allerton.2010.5706956
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发表时间：
2010-09
期刊：
影响因子：
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通讯作者：
Jing Wang;N. Elia
中科院分区：
文献类型：
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作者：
Jing Wang;N. Elia
在本文中，我们提出了一种新的计算模型来解决分布式优化问题的目标函数形成的凸函数的总和，以个人代理。我们的方法区别于现有的方法，局部凸混合和梯度搜索，我们迫使模型的状态的全局最优点，通过控制的次梯度的全局最优函数。这样，我们提出的模型不受梯度搜索中步长减小的限制，并允许快速渐近收敛。该模型还显示出对加性噪声的鲁棒性，这是基于凸混合或共识的算法的主要诅咒。
In this paper, we propose a novel computation model for solving the distributed optimization problem where the objective function is formed by the sum of convex functions available to individual agent. Our approach differentiates from the existing approach by local convex mixing and gradient searching in that we force the states of the model to the global optimal point by controlling the subgradient of the global optimal function. In this way, the model we proposed does not suffer from the limitation of diminishing step size in gradient searching and allows fast asymptotic convergence. The model also shows robustness to additive noise, which is a main curse for algorithms based on convex mixing or consensus.