Quantized Stochastic Primal-Dual Methods for Distributed Optimization under Relaxed Global Geometry 事件

Quantized Stochastic Primal-Dual Methods for Distributed Optimization under Relaxed Global Geometry arXiv:2606.11339v1 Announce Type: cross Abstract: We study distributed optimization with stochastic gradients and finite-bit communication modeled by random (unbiased) quantization. We propose q-PDGD, a quantized stochastic primal-dual method, and analyze it under relaxed global geometry. Under restricted secant inequality (RSI), a constant step-size yields linear contraction to an explicit neigh