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We build on the recently proposed EigenGame that views eigendecomposition asa competitive game. EigenGame's updates are biased if computed usingminibatches of data, which hinders convergence and more sophisticatedparallelism in the stochastic setting. In this work, we propose an unbiasedstochastic update that is asymptotically equivalent to EigenGame, enjoysgreater parallelism allowing computation on datasets of larger sample sizes,and outperforms EigenGame in experiments. We present applications to findingthe principal components of massive datasets and performing spectral clusteringof graphs. We analyze and discuss our proposed update in the context ofEigenGame and the shift in perspective from optimization to games.

EigenGame Unloaded: When playing games is better than optimizing