Stochastic L-BFGS Revisited: Improved Convergence Rates and Practical Acceleration Strategies

Renbo Zhao, William B. Haskell, Vincent Y. F. Tan
Proceedings of the 33rd Conference on Uncertainty in Artificial Intelligence, PMLR R15:740-749, 2017.

Abstract

We revisit the stochastic limited-memory BFGS (L-BFGS) algorithm. By proposing a new frame- work for analyzing convergence, we theoreti- cally improve the (linear) convergence rates and computational complexities of the stochastic L- BFGS algorithms in previous works. In addi- tion, we propose several practical acceleration strategies to speed up the empirical performance of such algorithms. We also provide theoretical analyses for most of the strategies. Experiments on large-scale logistic and ridge regression prob- lems demonstrate that our proposed strategies yield significant improvements via-‘a-vis compet- ing state-of-the-art algorithms.

Cite this Paper

BibTeX
@InProceedings{pmlr-vR15-zhao17b, title = {Stochastic L-{BFGS} Revisited: Improved Convergence Rates and Practical Acceleration Strategies}, author = {Zhao, Renbo and Haskell, William B. and Tan, Vincent Y. F.}, booktitle = {Proceedings of the 33rd Conference on Uncertainty in Artificial Intelligence}, pages = {740--749}, year = {2017}, editor = {Elidan, Gal and Kersting, Kristian}, volume = {R15}, series = {Proceedings of Machine Learning Research}, month = {11--15 Aug}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/r15/main/assets/zhao17b/zhao17b.pdf}, url = {https://proceedings.mlr.press/r15/zhao17b.html}, abstract = {We revisit the stochastic limited-memory BFGS (L-BFGS) algorithm. By proposing a new frame- work for analyzing convergence, we theoreti- cally improve the (linear) convergence rates and computational complexities of the stochastic L- BFGS algorithms in previous works. In addi- tion, we propose several practical acceleration strategies to speed up the empirical performance of such algorithms. We also provide theoretical analyses for most of the strategies. Experiments on large-scale logistic and ridge regression prob- lems demonstrate that our proposed strategies yield significant improvements via-‘a-vis compet- ing state-of-the-art algorithms.}, note = {Reissued by PMLR on 04 October 2026.} }
Endnote
%0 Conference Paper %T Stochastic L-BFGS Revisited: Improved Convergence Rates and Practical Acceleration Strategies %A Renbo Zhao %A William B. Haskell %A Vincent Y. F. Tan %B Proceedings of the 33rd Conference on Uncertainty in Artificial Intelligence %C Proceedings of Machine Learning Research %D 2017 %E Gal Elidan %E Kristian Kersting %F pmlr-vR15-zhao17b %I PMLR %P 740--749 %U https://proceedings.mlr.press/r15/zhao17b.html %V R15 %X We revisit the stochastic limited-memory BFGS (L-BFGS) algorithm. By proposing a new frame- work for analyzing convergence, we theoreti- cally improve the (linear) convergence rates and computational complexities of the stochastic L- BFGS algorithms in previous works. In addi- tion, we propose several practical acceleration strategies to speed up the empirical performance of such algorithms. We also provide theoretical analyses for most of the strategies. Experiments on large-scale logistic and ridge regression prob- lems demonstrate that our proposed strategies yield significant improvements via-‘a-vis compet- ing state-of-the-art algorithms. %Z Reissued by PMLR on 04 October 2026.
APA
Zhao, R., Haskell, W.B. & Tan, V.Y.F.. (2017). Stochastic L-BFGS Revisited: Improved Convergence Rates and Practical Acceleration Strategies. Proceedings of the 33rd Conference on Uncertainty in Artificial Intelligence, in Proceedings of Machine Learning Research R15:740-749 Available from https://proceedings.mlr.press/r15/zhao17b.html. Reissued by PMLR on 04 October 2026.

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