Smoothing Accelerated Proximal Gradient Method with Fast Convergence Rate for Nonsmooth Convex Optimization Beyond Differentiability-Reference-Cited by-同舟云学术

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Smoothing Accelerated Proximal Gradient Method with Fast Convergence Rate for Nonsmooth Convex Optimization Beyond Differentiability

Published:2023-03-04 Issue:2 Volume:197 Page:539-572
ISSN:0022-3239
Container-title:Journal of Optimization Theory and Applications
language:en
Short-container-title:J Optim Theory Appl

Author:

Wu Fan,Bian Wei^ORCID

Funder

National Natural Science Foundation of China

Key Technologies Research and Development Program

Fundamental Research Funds for the Central Universities

Publisher

Springer Science and Business Media LLC

Subject

Applied Mathematics,Management Science and Operations Research,Control and Optimization

Link

https://link.springer.com/content/pdf/10.1007/s10957-023-02176-6.pdf

Reference54 articles.

1. Adly, S., Attouch, H.: Finite convergence of proximal-gradient inertial algorithms combining dry friction with Hessian-driven damping. SIAM J. Optim. 30(3), 2134–2162 (2020)

2. Attouch, H., Cabot, A.: Convergence rate of a relaxed inertial proximal algorithm for convex minimization. Optimization 69(6), 1281–1312 (2020)

3. Attouch, H., Chbani, Z., Fadili, J., Riahi, H.: First-order optimization algorithms via inertial systems with Hessian driven damping. Math. Program. (2020). https://doi.org/10.1007/s10107-020-01591-1

4. Attouch, H., Chbani, Z., Peypouquet, J., Redont, P.: Fast convergence of inertial dynamics and algorithms with asymptotic vanishing viscosity. Math. Program. 168(1–2), 123–175 (2018)

5. Attouch, H., Chbani, Z., Riahi, H.: Fast proximal methods via time scaling of damped inertial dynamics. SIAM J. Optim. 29(3), 2227–2256 (2019)

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1. A feasible smoothing accelerated projected gradient method for nonsmooth convex optimization;Operations Research Letters;2024-11

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