3 papers
math.OC2025
Achieving Linear Speedup and Near-Optimal Complexity for Decentralized Optimization over Row-stochastic Networks
Liyuan Liang, Xinyi Chen, Gan Luo +1
A key challenge in decentralized optimization is determining the optimal convergence rate and designing algorithms to achieve it. While this problem has been extensively addressed…
math.OC2025
On the Linear Speedup of the Push-Pull Method for Decentralized Optimization over Digraphs
Liyuan Liang, Gan Luo, Kun Yuan
The linear speedup property is essential for demonstrating the advantage of distributed algorithms over their single-node counterparts. In this paper, we study the stochastic Push-…
math.OC2025
Efficient First-Order Optimization on the Pareto Set for Multi-Objective Learning under Preference Guidance
Lisha Chen, Quan Xiao, Ellen Hidemi Fukuda +3
Multi-objective learning under user-specified preference is common in real-world problems such as multi-lingual speech recognition under fairness. In this work, we frame such a pro…