Universal Regularization Methods: Varying the Power, the Smoothness and the Accuracy
Author:
Publisher
Society for Industrial & Applied Mathematics (SIAM)
Subject
Theoretical Computer Science,Software
Link
https://epubs.siam.org/doi/pdf/10.1137/16M1106316
Reference11 articles.
1. Worst-case evaluation complexity for unconstrained nonlinear optimization using high-order regularized models
2. The Use of Quadratic Regularization with a Cubic Descent Condition for Unconstrained Optimization
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 II: worst-case function- and derivative-evaluation complexity
5. Worst-case evaluation complexity of regularization methods for smooth unconstrained optimization using Hölder continuous gradients
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