论文标题
快速，稳定的非凸的约束分布式优化：Ellada算法
Fast and Stable Nonconvex Constrained Distributed Optimization: The ELLADA Algorithm
论文作者
论文摘要
分布式优化是使用多种代理以局部和协调方式进行计算的分布式优化，是解决大规模优化问题的一种有前途的方法，例如，在大型植物的模型预测控制（MPC）中产生的问题。但是，一种在计算上有效，全球收敛性，适合非convex约束的分布式优化算法和一般的累生系统相互作用仍然是一个开放的问题。在本文中，我们将三个重要的修改结合到经典的交替方向方法（ADMM）以进行分布式优化。具体而言，（i）采用超层架构来适应非概念性和处理不平等约束，（ii）允许允许解决等值的非线性编程（NLP）问题，并且（iii）修改后的Anderson加速可用于减少迭代次数。建立了针对拟议算法的固定解决方案和计算复杂性的理论收敛，称为Ellada。还通过基准过程系统描述和说明了其在分布式非线性MPC上的应用。
Distributed optimization, where the computations are performed in a localized and coordinated manner using multiple agents, is a promising approach for solving large-scale optimization problems, e.g., those arising in model predictive control (MPC) of large-scale plants. However, a distributed optimization algorithm that is computationally efficient, globally convergent, amenable to nonconvex constraints and general inter-subsystem interactions remains an open problem. In this paper, we combine three important modifications to the classical alternating direction method of multipliers (ADMM) for distributed optimization. Specifically, (i) an extra-layer architecture is adopted to accommodate nonconvexity and handle inequality constraints, (ii) equality-constrained nonlinear programming (NLP) problems are allowed to be solved approximately, and (iii) a modified Anderson acceleration is employed for reducing the number of iterations. Theoretical convergence towards stationary solutions and computational complexity of the proposed algorithm, named ELLADA, is established. Its application to distributed nonlinear MPC is also described and illustrated through a benchmark process system.
