A Line-Search Algorithm Inspired by the Adaptive Cubic Regularization Framework and Complexity Analysis
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DOI: 10.1007/s10957-018-1341-2
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References listed on IDEAS
- E. Bergou & Y. Diouane & S. Gratton, 2017. "On the use of the energy norm in trust-region and adaptive cubic regularization subproblems," Computational Optimization and Applications, Springer, vol. 68(3), pages 533-554, December.
- Nicholas Gould & Dominique Orban & Philippe Toint, 2015. "CUTEst: a Constrained and Unconstrained Testing Environment with safe threads for mathematical optimization," Computational Optimization and Applications, Springer, vol. 60(3), pages 545-557, April.
- J. M. Martínez & M. Raydan, 2017. "Cubic-regularization counterpart of a variable-norm trust-region method for unconstrained minimization," Journal of Global Optimization, Springer, vol. 68(2), pages 367-385, June.
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Cited by:
- J. M. Martínez & L. T. Santos, 2022. "On large-scale unconstrained optimization and arbitrary regularization," Computational Optimization and Applications, Springer, vol. 81(1), pages 1-30, January.
- Seonho Park & Seung Hyun Jung & Panos M. Pardalos, 2020. "Combining Stochastic Adaptive Cubic Regularization with Negative Curvature for Nonconvex Optimization," Journal of Optimization Theory and Applications, Springer, vol. 184(3), pages 953-971, March.
- C. P. Brás & J. M. Martínez & M. Raydan, 2020. "Large-scale unconstrained optimization using separable cubic modeling and matrix-free subspace minimization," Computational Optimization and Applications, Springer, vol. 75(1), pages 169-205, January.
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Keywords
Nonlinear optimization; Unconstrained optimization; Line-search methods; Adaptive regularized framework using cubics; Trust-region methods; Worst-case complexity;All these keywords.
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