ϵ-subgradient algorithms for bilevel convex optimization-Reference-Cited by-同舟云学术

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ϵ-subgradient algorithms for bilevel convex optimization

Published:2017-04-05 Issue:5 Volume:33 Page:055020
ISSN:0266-5611
Container-title:Inverse Problems
language:
Short-container-title:Inverse Problems

Author:

Helou Elias S,Simões Lucas E A

Funder

Conselho Nacional de Desenvolvimento Científico e Tecnológico

Fundação de Amparo a Pesquisa do Estado de São Paulo

Publisher

IOP Publishing

Subject

Applied Mathematics,Computer Science Applications,Mathematical Physics,Signal Processing,Theoretical Computer Science

Link

https://iopscience.iop.org/article/10.1088/1361-6420/aa6136/pdf

Reference38 articles.

1. A first order method for finding minimal norm-like solutions of convex optimization problems

2. A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems

3. Incremental proximal methods for large scale convex optimization

4. A New Class of Incremental Gradient Methods for Least Squares Problems

5. Gradient Convergence in Gradient methods with Errors

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1. Achieving Optimal Complexity Guarantees for a Class of Bilevel Convex Optimization Problems;2024 American Control Conference (ACC);2024-07-10

2. Convergence of distributed approximate subgradient method for minimizing convex function with convex functional constraints;AIMS Mathematics;2024

3. Convex Bi-level Optimization Problems with Nonsmooth Outer Objective Function;SIAM Journal on Optimization;2023-11-10

4. Optimal defense resource allocation against cyber-attacks in distributed generation systems;Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability;2023-10-26

5. Methodology and first-order algorithms for solving nonsmooth and non-strongly convex bilevel optimization problems;Mathematical Programming;2022-12-27

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