An Active Set Limited Memory BFGS Algorithm for Machine Learning
Hanger Liu, Yan Li, Maojun Zhang · Symmetry · 2022
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.