Accelerating Stochastic Newton Method via Chebyshev Polynomial Approximation

Author:

Sha Fan,Pan Jianyu

Publisher

Springer Nature Switzerland

Reference18 articles.

1. Blackard, J.A., Dean, D.J.: Comparative accuracies of artificial neural networks and discriminant analysis in predicting forest cover types from cartographic variables. Comput. Electron. Agric. 24(3), 131–151 (1999)

2. Defazio, A., Bach, F., Lacoste-Julien, S.: SAGA: a fast incremental gradient method with support for non-strongly convex composite objectives. In: Advances in Neural Information Processing Systems, pp. 1646–1654 (2014)

3. Erdogdu, M.A., Montanari, A.: Convergence rates of sub-sampled newton methods, pp. 3034–3042. Advances in Neural Information Processing Systems (2015)

4. Golub, G.H., van Loan, C.F.: Matrix Computations, 2nd edn. The Johns Hopkins University Press, Baltimore and London (1989)

5. Golub, G.H., Varga, R.S.: Chebyshev semi-iterative methods, successive overrelaxation iterative methods, and second order Richardson iterative methods. Numer. Math. 3, 147–156 (1961)

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