A Proximal Bundle Variant with Optimal Iteration-Complexity for a Large Range of Prox Stepsizes
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Publication:5013585
DOI10.1137/20M1327513MaRDI QIDQ5013585
Renato D. C. Monteiro, Jiaming Liang
Publication date: 1 December 2021
Published in: SIAM Journal on Optimization (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/2003.11457
Analysis of algorithms and problem complexity (68Q25) Numerical mathematical programming methods (65K05) Convex programming (90C25) Abstract computational complexity for mathematical programming problems (90C60) Nonlinear programming (90C30)
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Cites Work
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- Convex proximal bundle methods in depth: a unified analysis for inexact oracles
- Lectures on convex optimization
- Efficiency of proximal bundle methods
- Error bounds for proximal point subproblems and associated inexact proximal point algorithms
- Probabilistic optimization via approximate \(p\)-efficient points and bundle methods
- Non-Euclidean restricted memory level method for large-scale convex optimization
- Proximal level bundle methods for convex nondifferentiable optimization, saddle-point problems and variational inequalities
- New variants of bundle methods
- Descentwise inexact proximal algorithms for smooth optimization
- Rate of convergence of the bundle method
- Bundle-level type methods uniformly optimal for smooth and nonsmooth convex optimization
- A Nonmonotone Proximal Bundle Method with (Potentially) Continuous Step Decisions
- On the Complexity of the Hybrid Proximal Extragradient Method for the Iterates and the Ergodic Mean
- Constrained Bundle Methods for Upper Inexact Oracles with Application to Joint Chance Constrained Energy Problems
- Complexity of Variants of Tseng's Modified F-B Splitting and Korpelevich's Methods for Hemivariational Inequalities with Applications to Saddle-point and Convex Optimization Problems
- A modification and an extension of Lemarechal’s algorithm for nonsmooth minimization
- New Proximal Point Algorithms for Convex Minimization
- Monotone Operators and the Proximal Point Algorithm
- Catalyst Acceleration for First-order Convex Optimization: from Theory to Practice
- First-Order Methods in Optimization
- Incremental Bundle Methods using Upper Models
- Generalized Bundle Methods