An acceleration procedure for optimal first-order methods

DOI10.1080/10556788.2013.835812zbMath1282.90118arXiv1207.3951OpenAlexW2024891446MaRDI QIDQ5746718

Michel Baes, Michael Bürgisser

Publication date: 7 February 2014

Published in: Optimization Methods and Software (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1207.3951

zbMATH Keywords

convex optimizationeigenvalue optimizationfirst-order methods

Mathematics Subject Classification ID

Semidefinite programming (90C22) Convex programming (90C25) Large-scale problems in mathematical programming (90C06)

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Uses Software

• TFOCS

Cites Work

• Primal-dual subgradient methods for convex problems
• Smooth minimization of non-smooth functions
• A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems
• Gradient methods for minimizing composite functions
• Subgradient methods for huge-scale optimization problems
• Convex optimization methods for dimension reduction and coefficient estimation in multivariate linear regression
• Smoothing technique and its applications in semidefinite optimization
• Templates for convex cone problems with applications to sparse signal recovery
• Efficiency of Coordinate Descent Methods on Huge-Scale Optimization Problems
• Smooth Optimization with Approximate Gradient
• Robust Stochastic Approximation Approach to Stochastic Programming
• A Spectral Bundle Method for Semidefinite Programming
• Prox-Method with Rate of Convergence O(1/t) for Variational Inequalities with Lipschitz Continuous Monotone Operators and Smooth Convex-Concave Saddle Point Problems

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