A fast gradient and function sampling method for finite-max functions
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DOI: 10.1007/s10589-018-0030-2
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- Elias Salomão Helou & Sandra A. Santos & Lucas E. A. Simões, 2017. "On the Local Convergence Analysis of the Gradient Sampling Method for Finite Max-Functions," Journal of Optimization Theory and Applications, Springer, vol. 175(1), pages 137-157, October.
- J. V. Burke & A. S. Lewis & M. L. Overton, 2002. "Approximating Subdifferentials by Random Sampling of Gradients," Mathematics of Operations Research, INFORMS, vol. 27(3), pages 567-584, August.
- Milagros Loreto & Hugo Aponte & Debora Cores & Marcos Raydan, 2017. "Nonsmooth spectral gradient methods for unconstrained optimization," EURO Journal on Computational Optimization, Springer;EURO - The Association of European Operational Research Societies, vol. 5(4), pages 529-553, December.
- Jianzhong Zhang & Nae-Heon Kim & L. Lasdon, 1985. "An Improved Successive Linear Programming Algorithm," Management Science, INFORMS, vol. 31(10), pages 1312-1331, October.
- Peng, Chengbin & Jin, Xiaogang & Shi, Meixia, 2010. "Epidemic threshold and immunization on generalized networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(3), pages 549-560.
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Cited by:
- Morteza Maleknia & Mostafa Shamsi, 2020. "A Gradient Sampling Method Based on Ideal Direction for Solving Nonsmooth Optimization Problems," Journal of Optimization Theory and Applications, Springer, vol. 187(1), pages 181-204, October.
- M. Maleknia & M. Shamsi, 2020. "A new method based on the proximal bundle idea and gradient sampling technique for minimizing nonsmooth convex functions," Computational Optimization and Applications, Springer, vol. 77(2), pages 379-409, November.
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Keywords
Nonsmooth nonconvex optimization; Gradient sampling; Local superlinear convergence; Global convergence; Unconstrained minimization;All these keywords.
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