Approximate Newton Methods and Their Local Convergence
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Proceedings of the 34th International Conference on Machine Learning, PMLR 70:39313939, 2017.
Abstract
Many machine learning models are reformulated as optimization problems. Thus, it is important to solve a largescale 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.
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