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Approximate Newton Methods and Their Local Convergence
Haishan Ye · Luo Luo · Zhihua Zhang

Tue Aug 08 01:30 AM -- 05:00 AM (PDT) @ Gallery #12

Many machine learning models are reformulated as optimization problems. Thus, it is important to solve a large-scale optimization problem in big data applications. Recently, subsampled Newton methods have emerged to attract much attention for optimization due to their efficiency at each iteration, rectified a weakness in the ordinary Newton method of suffering a high cost in each iteration while commanding a high convergence rate. Other efficient stochastic second order methods are also proposed. However, the convergence properties of these methods are still not well understood. There are also several important gaps between the current convergence theory and performance in real applications. In this paper, we aim to fill these gaps. We propose a unifying framework to analyze local convergence properties of second order methods. Based on this framework, our theoretical analysis matches the performance in real applications.

Author Information

Haishan Ye (Shanghai Jiao Tong University)
Luo Luo (Shanghai Jiao Tong University)
Zhihua Zhang (Peking University)

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