Title: A Generalized EigenGame With Extensions to Deep Multiview Representation Learning. Abstract: Generalized Eigenvalue Problems (GEPs) encompass a range of interesting scientific computing problems. Canonical Correlation Analysis (CCA) and Partial Least Squares (PLS) are two such examples of GEPs which are often used to learn representations of multiview data. Development of efficient stochastic approaches to these problems would allow them to scale to large datasets. Furthermore, existing deep learning based extensions of CCA require large minibatch sizes in the stochastic setting to achieve good performance. Inspired by recent formulations of Principal Components Analysis and GEPs as games with differentiable utilities, we develop an alternative game theoretic approach to solving GEPs in which all constraints are softly enforced by Lagrange multipliers. We show that our approach shares much of the theoretical grounding of the previous game theoretic approaches but has fewer hyperparameters, is faster to converge, and permits extension to general function approximators like neural networks for certain GEPs including CCA. We demonstrate the effectiveness of our method for solving GEPs using canonical multiview datasets and demonstrate state-of-the-art performance for the Deep CCA problem for multiview representation learning.