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Linearly Convergent Second-Order Distributed Optimization Algorithms
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DOI：10.1109/TAC.2024.3360287.png)
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摘要
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This article studies distributed optimization problems whose goal is to minimize the sum of cost functions located among agents in a network, where communications are described by a connected and undirected graph. Two novel second-order methods with adapt-then-combine strategy are developed. For the algorithms, explicit convergence rates are established under strongly convex and the Lipschitz gradient assumptions. Finally, numerical examples demonstrate the efficiency of algorithms and are in line with theoretical results.
Keyword:
Convergence
Optimization
Costs
Three-dimensional displays
Taylor series
Linear programming
Lagrangian functions
Adapt-then-combine (ATC) diffusion
distributed optimization
primal-dual method
second-order method
期刊
IF:
7
论文数:
1.3W
被引数:
6.7W
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Distributed Nesterov Gradient and Heavy-Ball Double Accelerated Asynchronous Optimization分布式Nesterov梯度和重球双加速异步优化
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