Accelerated Smoothing Hard Thresholding Algorithms for $$\ell _0$$ Regularized Nonsmooth Convex Regression Problem-Reference-Cited by-同舟云学术

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Accelerated Smoothing Hard Thresholding Algorithms for $$\ell _0$$ Regularized Nonsmooth Convex Regression Problem

Published:2023-06-15 Issue:2 Volume:96 Page:
ISSN:0885-7474
Container-title:Journal of Scientific Computing
language:en
Short-container-title:J Sci Comput

Author:

Bian Wei,Wu Fan

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

Computational Theory and Mathematics,General Engineering,Theoretical Computer Science,Software,Applied Mathematics,Computational Mathematics,Numerical Analysis

Link

https://link.springer.com/content/pdf/10.1007/s10915-023-02249-8.pdf

Reference46 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. Alecsa, C.D., László, S.C., Pinţa, T.: An extension of the second order dynamical system that model Nesterov’s convex gradient method. Appl. Math. Optim. 84(2), 1687–1716 (2021)

3. Attouch, H., László, S.: Newton-like inertial dynamics and proximal algorithms governed by maximally monotone operators. SIAM J. Optim. 30(4), 3252–3283 (2020)

4. Attouch, H., Peypouquet, J.: The rate of convergence of Nesterov’s accelerated forward-backward method is actually faster than $$1/k^2$$. SIAM J. Optim. 26(3), 1824–1834 (2016)

5. Beck, A., Teboulle, M.: A fast iterative shrinkage-thresholding algorithm for linear inverse problems. SIAM J. Imaging Sci. 2(1), 183–202 (2009)

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