Papers
Topics
Authors
Recent
Assistant
AI Research Assistant
Well-researched responses based on relevant abstracts and paper content.
Custom Instructions Pro
Preferences or requirements that you'd like Emergent Mind to consider when generating responses.
Gemini 2.5 Flash
Gemini 2.5 Flash 173 tok/s
Gemini 2.5 Pro 54 tok/s Pro
GPT-5 Medium 36 tok/s Pro
GPT-5 High 35 tok/s Pro
GPT-4o 110 tok/s Pro
Kimi K2 221 tok/s Pro
GPT OSS 120B 444 tok/s Pro
Claude Sonnet 4.5 37 tok/s Pro
2000 character limit reached

Solving Non-Convex Non-Concave Min-Max Games Under Polyak-Łojasiewicz Condition (1812.02878v1)

Published 7 Dec 2018 in math.OC, cs.GT, and cs.LG

Abstract: In this short note, we consider the problem of solving a min-max zero-sum game. This problem has been extensively studied in the convex-concave regime where the global solution can be computed efficiently. Recently, there have also been developments for finding the first order stationary points of the game when one of the player's objective is concave or (weakly) concave. This work focuses on the non-convex non-concave regime where the objective of one of the players satisfies Polyak-{\L}ojasiewicz (PL) Condition. For such a game, we show that a simple multi-step gradient descent-ascent algorithm finds an $\varepsilon$--first order stationary point of the problem in $\widetilde{\mathcal{O}}(\varepsilon{-2})$ iterations.

Citations (34)

Summary

We haven't generated a summary for this paper yet.

Lightbulb Streamline Icon: https://streamlinehq.com

Continue Learning

We haven't generated follow-up questions for this paper yet.

List To Do Tasks Checklist Streamline Icon: https://streamlinehq.com

Collections

Sign up for free to add this paper to one or more collections.