arXiv (All)
open access
Variance-reduced accelerated methods for decentralized stochastic double-regularized nonconvex strongly-concave minimax problems
In this paper, we consider the decentralized, stochastic nonconvex strongly-concave (NCSC) minimax problem with nonsmooth regularization terms on both primal and dual variables, wherein a network of $m$ computing agents collaborate via peer-to-peer communications. We consider when the coupling funct
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Decentralized Nonconvex Optimization under Heavy-Tailed Noise: Normalization and Optimal Convergence#147084
- Proximal Nonlinear Conjugate Gradient Methods for Composite Optimization#677562
- Proximal Nonlinear Conjugate Gradient Methods for Composite Optimization#674363
- Stochastic Bilevel Optimization with Heavy-Tailed Noise#1104884
- A Hybrid Subgradient Method for Nonsmooth Nonconvex Bilevel Optimization#160891
Record · ID 1187190
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive