Accelerated First-Order Methods for Convex Optimization with Locally Lipschitz Continuous Gradient
Author:
Affiliation:
1. Department of Industrial and Systems Engineering, University of Minnesota, Minneapolis, MN 55455-0153 USA.
Funder
National Natural Science Foundation of China
Publisher
Society for Industrial & Applied Mathematics (SIAM)
Subject
Theoretical Computer Science,Software,Applied Mathematics
Reference18 articles.
1. A Descent Lemma Beyond Lipschitz Gradient Continuity: First-Order Methods Revisited and Applications
2. A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems
3. Accelerated, Parallel, and Proximal Coordinate Descent
4. Accelerated Bregman proximal gradient methods for relatively smooth convex optimization
5. Nearly Optimal First-Order Methods for Convex Optimization under Gradient Norm Measure: an Adaptive Regularization Approach
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