Evaluation complexity of adaptive cubic regularization methods for convex unconstrained optimization
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Publication:2885470
DOI10.1080/10556788.2011.602076zbMath1252.90061OpenAlexW2134971818WikidataQ58185690 ScholiaQ58185690MaRDI QIDQ2885470
Coralia Cartis, Nicholas I. M. Gould, Phillipe L. Toint
Publication date: 23 May 2012
Published in: Optimization Methods and Software (Search for Journal in Brave)
Full work available at URL: http://purl.org/net/epubs/work/54091
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Cites Work
- Unnamed Item
- Adaptive cubic regularisation methods for unconstrained optimization. I: Motivation, convergence and numerical results
- Accelerating the cubic regularization of Newton's method on convex problems
- Cubic regularization of Newton method and its global performance
- On the Complexity of Steepest Descent, Newton's and Regularized Newton's Methods for Nonconvex Unconstrained Optimization Problems
- Trust Region Methods
- Affine conjugate adaptive Newton methods for nonlinear elastomechanics