scholarly journals A First-Order Primal-Dual Algorithm with Linesearch

2018 ◽  
Vol 28 (1) ◽  
pp. 411-432 ◽  
Author(s):  
Yura Malitsky ◽  
Thomas Pock
2012 ◽  
Vol 57 (4) ◽  
pp. 1419-1428 ◽  
Author(s):  
Xingju Cai ◽  
Deren Han ◽  
Lingling Xu

2015 ◽  
Vol 159 (1-2) ◽  
pp. 253-287 ◽  
Author(s):  
Antonin Chambolle ◽  
Thomas Pock

2017 ◽  
Vol 38 (5) ◽  
pp. 602-626 ◽  
Author(s):  
Lorenzo Rosasco ◽  
Silvia Villa ◽  
Bằng Công Vũ

2021 ◽  
Vol 0 (0) ◽  
pp. 0
Author(s):  
Kai Wang ◽  
Deren Han

<p style='text-indent:20px;'>In this paper, we consider the general first order primal-dual algorithm, which covers several recent popular algorithms such as the one proposed in [Chambolle, A. and Pock T., A first-order primal-dual algorithm for convex problems with applications to imaging, J. Math. Imaging Vis., 40 (2011) 120-145] as a special case. Under suitable conditions, we prove its global convergence and analyze its linear rate of convergence. As compared to the results in the literature, we derive the corresponding results for the general case and under weaker conditions. Furthermore, the global linear rate of the linearized primal-dual algorithm is established in the same analytical framework.</p>


2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Feng Ma ◽  
Mingfang Ni ◽  
Lei Zhu ◽  
Zhanke Yu

Many application problems of practical interest can be posed as structured convex optimization models. In this paper, we study a new first-order primaldual algorithm. The method can be easily implementable, provided that the resolvent operators of the component objective functions are simple to evaluate. We show that the proposed method can be interpreted as a proximal point algorithm with a customized metric proximal parameter. Convergence property is established under the analytic contraction framework. Finally, we verify the efficiency of the algorithm by solving the stable principal component pursuit problem.


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