2 papers
math.OC2026
D-ripALM: A Tuning-friendly Decentralized Relative-Type Inexact Proximal Augmented Lagrangian Method
Jiayi Zhu, Hong Wang, Ling Liang +1
This paper proposes D-ripALM, a Decentralized relative-type inexact proximal Augmented Lagrangian Method for consensus convex optimization over multi-agent networks. D-ripALM adopt…
math.OC2025
Exact Decentralized Optimization via Explicit Consensus Penalties
Hong Wang
Consensus optimization enables autonomous agents to solve joint tasks through peer-to-peer exchanges alone. Classical decentralized gradient descent is appealing for its minimal st…