3 papers
math.OC2026
A Unified Zeroth-Order Approach for Decentralized Minimax Optimization
Haoyuan Cai, Yike Zhao, Aleksandar Armacki +2
We propose ZOMA, a unified Zeroth-Order decentralized accelerated MinimAx framework for multi-agent nonconvex Polyak--Åojasiewicz minimax optimization. The proposed framework only…
eess.SP2026
Distributed Riemannian Optimization in Geodesically Non-convex Environments
Xiuheng Wang, Ricardo Borsoi, Cédric Richard +1
This paper studies the problem of distributed Riemannian optimization over a network of agents whose cost functions are geodesically smooth but possibly geodesically non-convex. Ex…
cs.LG2026
High-Probability Convergence in Decentralized Stochastic Optimization with Gradient Tracking
Aleksandar Armacki, Haoyuan Cai, Ali H. Sayed
We study high-probability (HP) convergence guarantees in decentralized stochastic optimization, where multiple agents collaborate to jointly train a model over a network. Existing…