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 171 tok/s
Gemini 2.5 Pro 52 tok/s Pro
GPT-5 Medium 38 tok/s Pro
GPT-5 High 43 tok/s Pro
GPT-4o 108 tok/s Pro
Kimi K2 173 tok/s Pro
GPT OSS 120B 442 tok/s Pro
Claude Sonnet 4.5 34 tok/s Pro
2000 character limit reached

Path-Following Methods for Generalized Nash Equilibrium Problems (2110.10627v1)

Published 20 Oct 2021 in math.OC

Abstract: Building upon the results in [Hinterm\"uller et al., SIAM J. Optim, '15], generalized Nash equilibrium problems are considered, in which the feasible set of each player is influenced by the decisions of their competitors. This is realized via the existence of one (or more) state constraint(s) establishing a link between the players. Special emphasis is put on the situation of a state encoded in a possibly non-linear operator equation. First order optimality conditions under a constraint qualification are derived. Aiming at a practically meaningful method, an approximation scheme using a penalization technique leading to a sequence of (Nash) equilibrium problems without dependence of the constraint set on the other players' strategies is established. An associated path-following strategy related to a value function is then proposed. This happens at first on the most abstract level and is subsequently established to a narrower framework geared to the presence of partial differential equations in the constraint. Our findings are illustrated with examples having distributed and boundary controls - both involving semi-linear elliptic PDEs.

Summary

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

Dice Question Streamline Icon: https://streamlinehq.com

Open Problems

We haven't generated a list of open problems mentioned in this paper yet.

Lightbulb Streamline Icon: https://streamlinehq.com

Continue Learning

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

Authors (1)

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

Collections

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