Relatively Smooth Convex Optimization by First-Order Methods, and Applications-Reference-Cited by-同舟云学术

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Relatively Smooth Convex Optimization by First-Order Methods, and Applications

Published:2018-01 Issue:1 Volume:28 Page:333-354
ISSN:1052-6234
Container-title:SIAM Journal on Optimization
language:en
Short-container-title:SIAM J. Optim.

Author:

Lu Haihao,Freund Robert M.,Nesterov Yurii

Funder

Université Catholique de Louvain

Air Force Office of Scientific Research

Publisher

Society for Industrial & Applied Mathematics (SIAM)

Subject

Theoretical Computer Science,Software

Link

https://epubs.siam.org/doi/pdf/10.1137/16M1099546

Reference19 articles.

1. Linear convergence of a modified Frank–Wolfe algorithm for computing minimum-volume enclosing ellipsoids

2. Large step volumetric potential reduction algorithms for linear programming

3. The Volumetric Barrier for Semidefinite Programming

4. Optimal and Efficient Designs of Experiments

5. A Descent Lemma Beyond Lipschitz Gradient Continuity: First-Order Methods Revisited and Applications

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