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Award ID contains: 2324643

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  1. ; ; (, IMA Journal of Numerical Analysis)
    Abstract Simple stochastic momentum methods are widely used in machine learning optimization, but their good practical performance is at odds with an absence of theoretical guarantees of acceleration in the literature. In this work, we aim to close the gap between theory and practice by showing that stochastic heavy ball momentum retains the fast linear rate of (deterministic) heavy ball momentum on quadratic optimization problems, at least when minibatching with a sufficiently large batch size. The algorithm we study can be interpreted as an accelerated randomized Kaczmarz algorithm with minibatching and heavy ball momentum. The analysis relies on carefully decomposing the momentum transition matrix, and using new spectral norm concentration bounds for products of independent random matrices. We provide numerical illustrations demonstrating that our bounds are reasonably sharp. 
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  2. ; ; (, Computational Optimization and Applications)
    Free, publicly-accessible full text available July 1, 2026
  3. ; ; (, Journal of Machine Learning Research)
    Free, publicly-accessible full text available April 30, 2026
  4. ; ; (, Journal of Machine Learning Research)
    Free, publicly-accessible full text available April 15, 2026
  5. ; (, IEEE Transactions on Automatic Control)
    Free, publicly-accessible full text available January 1, 2026
  6. ; ; (, SIAM Journal on Optimization)
    Free, publicly-accessible full text available December 31, 2025
  7. ; ; (, Journal of Optimization Theory and Applications)
    Free, publicly-accessible full text available December 1, 2025