In this paper, a stochastic quasi-Newton algorithm for nonconvex stochastic optimization is presented. It is derived from a classical modified BFGS formula. The update formula can be extended to the… Click to show full abstract
In this paper, a stochastic quasi-Newton algorithm for nonconvex stochastic optimization is presented. It is derived from a classical modified BFGS formula. The update formula can be extended to the framework of limited memory scheme. Numerical experiments on some problems in machine learning are given. The results show that the proposed algorithm has great prospects.
Keywords:
active set;
limited memory;
algorithm;
machine learning
Journal Title: Symmetry
Year Published: 2022
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Journal Title: Symmetry
Year Published: 2022
Link to full text (if available)
Share on Social Media: Sign Up to like & get
recommendations!