Newton Methods in Generalized Nash Equilibrium Problems with Applications to Game-Theoretic Model Predictive Control
Mushuang Liu, Ilya Kolmanovsky

TL;DR
This paper establishes input-to-state stability for perturbed Newton methods solving generalized Nash equilibrium problems, enabling fast, inexact solution tracking in dynamic systems like game-theoretic model predictive control, with theoretical guarantees and practical applications.
Contribution
The paper introduces ISS analysis for perturbed Newton methods in GNE problems, extending stability results under less restrictive conditions and applying them to real-time game-theoretic MPC.
Findings
ISS of perturbed Newton methods is proven for GNE problems.
Agent-distributed algorithms are developed for equilibrium computation.
Bounded tracking errors are demonstrated in real-time applications.
Abstract
We prove input-to-state stability (ISS) of perturbed Newton-type methods for generalized equations arising from Nash equilibrium (NE) and generalized NE (GNE) problems. This ISS property allows the use of inexact computations in equilibrium-seeking to enable fast solution tracking in dynamic systems such as in model predictive control (MPC). For NE problems, we address the local convergence of perturbed Josephy-Newton methods from the variational inequality (VI) stability analysis, and establish the ISS result under less restrictive regularity conditions compared to the existing results established for nonlinear optimization. Agent-distributed algorithms are also developed. For GNE problems, since they cannot be reduced to VI problems in general, we use semismooth Newton methods to solve the semismooth equations arising from the Karush-Kuhn-Tucker (KKT) systems of the GNE problem and…
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Taxonomy
TopicsAdvanced Control Systems Optimization · Optimization and Variational Analysis · Economic theories and models
