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A sequence-form differentiable path-following method to compute Nash equilibria

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

Abstract

The sequence-form representation has shown remarkable efficacy in computing Nash equilibria for two-player extensive-form games with perfect recall. Nonetheless, devising an efficient algorithm for n-player games using the sequence form remains a substantial challenge. To bridge this gap, we establish a necessary and sufficient condition, characterized by a polynomial system, for Nash equilibrium within the sequence-form framework. Building upon this, we develop a sequence-form differentiable path-following method for computing a Nash equilibrium. This method involves constructing an artificial logarithmic-barrier game in sequence form, where two functions of an auxiliary variable are introduced to incorporate logarithmic-barrier terms into the payoff functions and construct the strategy space. Afterward, we prove the existence of a smooth path determined by the artificial game, originating from an arbitrary totally mixed behavioral-strategy profile and converging to a Nash equilibrium of the original game as the auxiliary variable approaches zero. In addition, a convex-quadratic-penalty method and a variant of linear tracing procedure in sequence form are presented as two alternative techniques for computing a Nash equilibrium. Numerical comparisons further illuminate the effectiveness and efficiency of these methods. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.
Original languageEnglish
Pages (from-to)265-300
JournalComputational Optimization and Applications
Volume92
Issue number1
Online published17 Jun 2025
DOIs
Publication statusPublished - Sept 2025

Funding

This work was partially supported by CRF: CityU 11306821 of Hong Kong SAR Government.

Research Keywords

  • Extensive-form game
  • Sequence form
  • Nash equilibrium
  • Differentiable path-following method

RGC Funding Information

  • RGC-funded

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