873 citations · 5.4k across the 120 of their papers we have counts for
62 papers · 1 filter
Restarted Reflected Halpern Acceleration for Augmented Primal-Dual Methods
Benqi Liu, Ju Cao, Wotao Yin +1
We study linearly constrained composite convex optimization with a smooth term and a proximable nonsmooth term. We develop a unified augmented primal-dual framework with primal-dua…
Communication-Efficient Topologies for Decentralized Learning with Consensus Rate
Zhuoqing Song, Weijian Li, Kexin Jin +4
Decentralized optimization is an emerging paradigm in distributed learning in which agents achieve network-wide solutions by peer-to-peer communication without the central server.…
From the simplex to the sphere: Faster constrained optimization using the Hadamard parametrization
Qiuwei Li, Daniel McKenzie, Wotao Yin
The standard simplex in R^n, also known as the probability simplex, is the set of nonnegative vectors whose entries sum up to 1. They frequently appear as constraints in optimizati…
Curvature-Aware Derivative-Free Optimization
Bumsu Kim, HanQin Cai, Daniel McKenzie +1
The paper discusses derivative-free optimization (DFO), which involves minimizing a function without access to gradients or directional derivatives, only function evaluations. Clas…
Unified Convergence Analysis for Adaptive Optimization with Moving Average Estimator
Zhishuai Guo, Yi Xu, Wotao Yin +2
Although adaptive optimization algorithms have been successful in many applications, there are still some mysteries in terms of convergence analysis that have not been unraveled. T…
Learning to Optimize: A Primer and A Benchmark
Tianlong Chen, Xiaohan Chen, Wuyang Chen +4
Learning to optimize (L2O) is an emerging approach that leverages machine learning to develop optimization methods, aiming at reducing the laborious iterations of hand engineering.…