atlas

Research map / Optimization / Nonconvex and smooth optimization

A Catalyst Framework for Minimax Optimization

NeurIPS 2020 · Optimization › Nonconvex and smooth optimization · approach: Proximal finite-sum methods · 22 citations (OpenAlex)

Read the paperCheck your idea against 108,967 papers

AI-extracted summary. Written by AI from the paper's title only (no summary yet). Numbers and claims can be wrong or missing context, so check the paper before you cite or build on it.

Measured effects reported in the paper

What was changedWhereMeasuredEffect
Wrap extragradient in the Catalyst scheme with tau=mu, instead of running EG directly, using same stepsizesEG · Wireless channel capacity SC-C minimax, two synthetic datasetsconvergence vs gradient countHelped
Use Catalyst-EG instead of DIAGCatalyst-EG · Wireless channel capacity, Dataset 2convergence vs gradient countHelped
Wrap SVRG in the Catalyst schemeSVRG · Wireless channel capacity, Dataset 1convergence vs gradient countHelped

Closest papers by meaning

Writing a paper in this area? scime draft is a free LaTeX editor with AI included, real-time co-authors and a check of your references and venue rules before you submit. It also imports OpenAI Prism projects.

Open scime draft

Get the next scime Dispatch and a monthly field update

Free. We send a confirmation link first; every email has a one-click unsubscribe. Privacy