arXiv (All)
open access
Stochastic Gradient Descent Ascent is Suboptimal for Nonconvex-PL Min-Max Games
How far can stochastic gradient descent ascent (SGDA) go by tuning its timescale ratio and step sizes in nonconvex min-max games? We answer this question for nonconvex-PL (NC-PL) games by establishing the first tight complexity of two-timescale SGDA with a fixed timescale ratio and non-increasing st
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
- An Analysis of Scaling Issues in MPLS-TE Core Networks#209095
- On the Equality of the ELBO to a Sum of Entropies at Stationary Points of Learning#925693
- The Endpoint Fractional Riesz Estimate on the Hamming Cube#926847
- Hierarchical Time-aware Bootstrapping for Off-Policy Subgoal Value Learning#1201459
- Itô versus Hänggi-Klimontovich#149143
Record · ID 1217189
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