Search Results for author:
Found 7 papers, 1 papers with code
Diffusion Stochastic Optimization for Min-Max Problems
The optimistic gradient method is useful in addressing minimax optimization problems.
Revisiting Decentralized ProxSkip: Achieving Linear Speedup
We demonstrate that the leading communication complexity of ProxSkip is $\mathcal{O}\left(\frac{p\sigma^2}{n\epsilon^2}\right)$ for non-convex and convex settings, and $\mathcal{O}\left(\frac{p\sigma^2}{n\epsilon}\right)$ for the strongly convex setting, where $n$ represents the number of nodes, $p$ denotes the probability of communication, $\sigma^2$ signifies the level of stochastic noise, and $\epsilon$ denotes the desired accuracy level.
On the Performance of Gradient Tracking with Local Updates
We study the decentralized optimization problem where a network of $n$ agents seeks to minimize the average of a set of heterogeneous non-convex cost functions distributedly.
Removing Data Heterogeneity Influence Enhances Network Topology Dependence of Decentralized SGD
For smooth objective functions, the transient stage (which measures the number of iterations the algorithm has to experience before achieving the linear speedup stage) of D-SGD is on the order of ${\Omega}(n/(1-\beta)^2)$ and $\Omega(n^3/(1-\beta)^4)$ for strongly and generally convex cost functions, respectively, where $1-\beta \in (0, 1)$ is a topology-dependent quantity that approaches $0$ for a large and sparse network.
A Multi-Agent Primal-Dual Strategy for Composite Optimization over Distributed Features
This work studies multi-agent sharing optimization problems with the objective function being the sum of smooth local functions plus a convex (possibly non-smooth) function coupling all agents.
On the Influence of Bias-Correction on Distributed Stochastic Optimization
It is still unknown {\em whether}, {\em when} and {\em why} these bias-correction methods can outperform their traditional counterparts (such as consensus and diffusion) with noisy gradient and constant step-sizes.
Distributed Coupled Multi-Agent Stochastic Optimization
In this formulation, each agent is influenced by only a subset of the entries of a global parameter vector or model, and is subject to convex constraints that are only known locally.
Optimization and Control
