Worst-case evaluation complexity of regularization methods for smooth unconstrained optimization using Hölder continuous gradients
Author:
Affiliation:
1. Mathematical Institute, Oxford University, Oxford, UK
2. Numerical Analysis Group, Rutherford Appleton Laboratory, Chilton, UK
3. Namur Center for Complex Systems (naXys) and Department of Mathematics, University of Namur, Namur, Belgium
Funder
Engineering and Physical Sciences Research Council
Publisher
Informa UK Limited
Subject
Applied Mathematics,Control and Optimization,Software
Link
https://www.tandfonline.com/doi/pdf/10.1080/10556788.2016.1268136
Reference18 articles.
1. Regularity Results for Nonlinear Elliptic Systems and Applications
2. Trust-region and other regularisations of linear least-squares problems
3. On the Complexity of Steepest Descent, Newton's and Regularized Newton's Methods for Nonconvex Unconstrained Optimization Problems
4. Adaptive cubic regularisation methods for unconstrained optimization. Part I: motivation, convergence and numerical results
5. Adaptive cubic regularisation methods for unconstrained optimization. Part II: worst-case function- and derivative-evaluation complexity
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3. The Impact of Noise on Evaluation Complexity: The Deterministic Trust-Region Case;Journal of Optimization Theory and Applications;2023-01-09
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