.png){kind=icon}
.png){kind=icon}
Return
Distributed approximate Newton algorithms and weight design for constrained optimization
.png){kind=icon}
OA
AI
DOI：10.1016/j.automatica.2019.108538.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Motivated by economic dispatch and linearly-constrained resource allocation problems, this paper proposes a class of novel DISTRIBUTED APPROX-NEWTON algorithms that approximate the standard Newton optimization method. We first develop the notion of an optimal edge weighting for the communication graph over which agents implement the second-order algorithm, and propose a convex approximation for the nonconvex weight design problem. We next build on the optimal weight design to develop a DISCRETE DISTRIBUTED APPROX-NEWTON algorithm which converges linearly to the optimal solution for economic dispatch problems with unknown cost functions and relaxed local box constraints. For the full box-constrained problem, we develop a CONTINUOUS DISTRIBUTED APPROX-NEWTON algorithm which is inspired by first-order saddle-point methods and rigorously prove its convergence to the primal and dual optimizers. A main property of each of these distributed algorithms is that they only require agents to exchange constant-size communication messages, which lends itself to scalable implementations. Simulations demonstrate that the DISTRIBUTED APPROX-NEWTON algorithms with our weight design have superior convergence properties compared to existing weighting strategies for first-order saddle-point and gradient descent methods. (C) 2019 Elsevier Ltd. All rights reserved.
Keywords:
Distributed optimization
Multi-agent systems
Resource allocation
Networked systems
Second-order methods
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
5.9
Papers:
1.2W
Citations:
5.2W
