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arXiv · 1804.00325

Aggregated Momentum: Stability Through Passive Damping

James Lucas·Shengyang Sun·Richard Zemel·Roger Grosse
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Abstract

Momentum is a simple and widely used trick which allows gradient-based optimizers to pick up speed along low curvature directions. Its performance depends crucially on a damping coefficient $\beta$. Large $\beta$ values can potentially deliver much larger speedups, but are prone to oscillations and instability; hence one typically resorts to small values such as 0.5 or 0.9. We propose Aggregated Momentum (AggMo), a variant of momentum which combines multiple velocity vectors with different $\beta$ parameters. AggMo is trivial to implement, but significantly dampens oscillations, enabling it to remain stable even for aggressive $\beta$ values such as 0.999. We reinterpret Nesterov's accelerated gradient descent as a special case of AggMo and analyze rates of convergence for quadratic objectives. Empirically, we find that AggMo is a suitable drop-in replacement for other momentum methods, and frequently delivers faster convergence.

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BibTeXRIS

James Lucas, Shengyang Sun, Richard Zemel, Roger Grosse. 2018-04-01. Aggregated Momentum: Stability Through Passive Damping. https://arxiv.org/abs/1804.00325

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