A robust multi-batch L-BFGS method for machine learning
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Publication:4972551
DOI10.1080/10556788.2019.1658107zbMath1430.90523arXiv1707.08552OpenAlexW2970227300WikidataQ127324376 ScholiaQ127324376MaRDI QIDQ4972551
Albert S. Berahas, Martin Takáč
Publication date: 25 November 2019
Published in: Optimization Methods and Software (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1707.08552
Numerical mathematical programming methods (65K05) Large-scale problems in mathematical programming (90C06) Nonlinear programming (90C30) Methods of quasi-Newton type (90C53) Computational aspects of data analysis and big data (68T09)
Related Items (10)
Quasi-Newton methods for machine learning: forget the past, just sample ⋮ Limited-memory BFGS with displacement aggregation ⋮ Nonmonotone diagonally scaled limited-memory BFGS methods with application to compressive sensing based on a penalty model ⋮ An overview of stochastic quasi-Newton methods for large-scale machine learning ⋮ Globally Convergent Multilevel Training of Deep Residual Networks ⋮ Adaptive sampling quasi-Newton methods for zeroth-order stochastic optimization ⋮ Trust-region algorithms for training responses: machine learning methods using indefinite Hessian approximations ⋮ A robust multi-batch L-BFGS method for machine learning ⋮ Diagonally scaled memoryless quasi-Newton methods with application to compressed sensing ⋮ LSOS: Line-search second-order stochastic optimization methods for nonconvex finite sums
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