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

DOI10.1109/TSP.2017.2784360zbMath1414.94746arXiv1704.00116OpenAlexW2727020975MaRDI QIDQ4621626

Renbo Zhao, William B. Haskell, Vincent Y. F. Tan

Publication date: 12 February 2019

Published in: IEEE Transactions on Signal Processing (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1704.00116

Mathematics Subject Classification ID

Signal theory (characterization, reconstruction, filtering, etc.) (94A12)

Related Items (3)

A stochastic extra-step quasi-Newton method for nonsmooth nonconvex optimization ⋮ An overview of stochastic quasi-Newton methods for large-scale machine learning ⋮ Secant penalized BFGS: a noise robust quasi-Newton method via penalizing the secant condition

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