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Distributed Prediction-Correction Algorithm for Convex Optimization With Coupled Constraints

delete2025-09-01
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PRE
AI
X
X. P. Xu
Q
Qing Yuan
J
Jun Li
D
Dawen Xia
H
Huaqing Li *
DOI：10.1002/oca.70008delete
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Abstract

Abstract

En 中文
In this article, we propose an optimization algorithm based on a distributed predictive-correction framework, called DPCA, for solving coupled constrained convex optimization problems in undirected connected multi-agent networks. Based on the augmented Lagrangian, the algorithm which combines the generalized proximal point algorithm and the idea of the unified prediction-correction framework can achieve global optimization through local communication and computation in the case of incomplete information sharing. We demonstrate that DPCA attains a convergence rate of regarding both optimality and feasibility. Finally, the simulation results show that DPCA has better convergence performance and smaller convergence error than other algorithms.
Keywords:
distributed convex optimization
primal-dual method
sublinear convergence

Journal

Optimal Control Applications and Methods cover
Optimal Control Applications and Methods
IF:
1.5
Papers:
39
Citations:
2.2K

Organization

No organization information available