A global Newton method to compute Nash equilibria

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Empirically Evaluating Multiagent Learning Algorithms (2014, methodology): “For GIGA-WoLF ’s retraction map operation (the function that maps an arbitrary vector in ℜ n to the closest probability vector in terms of ℓ 2 distance) we used an algorithm based on the method described in Govindan and Wilson (2003).”
The State of Solving Large Incomplete-Information Games, and Application to Poker (2010, unknown): “To my knowledge the best equilibrium-finding algorithms for general multiplayer incompleteinformation games are continuation methods (Govindan and Wilson 2003).”
Computable Markov-Perfect Industry Dynamics: Existence, Purification, and Multiplicity (2007, unknown): “, Berry & Pakes 1993, Gowrisankaran 1999a, Fershtman & Pakes 2000) and it provides a starting point for ongoing research in industrial organization (see Doraszelski & Pakes (2007) for a survey) as well as in other fields such as international trade (Erdem & Tybout 2003) and finance (Goettler, Parlou”
Robust game theory (2006, methodology): “Govindan’s and Wilson’s [23] global Newton method similarly uses the multilinear system formulation.”
Simple search methods for finding a Nash equilibrium (2004, background): “by Govindan and Wilson (2003) and extended and efficiently implemented by”
Run the GAMUT: a comprehensive approach to evaluating game-theoretic algorithms (2004, methodology): “Govindan and Wilson [5] introduced an alternative algorithm based on a continuation method.”