Momentum Stiefel Optimizer, with Applications to Suitably-Orthogonal Attention, and Optimal Transport
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The problem of optimization on Stiefel manifold, i.e., minimizing functions of (not necessarily square) matrices that satisfy orthogonality constraints, has been extensively studied, partly due to rich machine learning applications. Yet, a new approach is proposed based on, for the first time, an interplay between thoughtfully designed continuous and discrete dynamics. It leads to a gradient-based optimizer with intrinsically added momentum. This method exactly preserves the manifold structure but does not require commonly used projection or retraction, and thus having low computational costs when compared to existing algorithms. Its generalization to adaptive learning rates is also demonstrated. Pleasant performances are observed in various practical tasks. For instance, we discover that placing orthogonal constraints on attention heads of trained-from-scratch Vision Transformer [Dosovitskiy et al. 2022] could remarkably improve its performance, when our optimizer is used, and it is better that each head is made orthogonal within itself but not necessarily to other heads. This optimizer also makes the useful notion of Projection Robust Wasserstein Distance [Paty Cuturi 2019][Lin et al. 2020] for high-dim. optimal transport even more effective.
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