Zobrazeno 1 - 4
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pro vyhledávání: '"Zhao, Jing"'
In this paper, for solving a broad class of large-scale nonconvex and nonsmooth optimization problems, we propose a stochastic two step inertial Bregman proximal alternating linearized minimization (STiBPALM) algorithm with variance-reduced stochasti
Externí odkaz:
http://arxiv.org/abs/2307.05287
Autor:
Guo, Chenzheng, Zhao, Jing
In this paper, we study an algorithm for solving a class of nonconvex and nonsmooth nonseparable optimization problems. Based on proximal alternating linearized minimization (PALM), we propose a new iterative algorithm which combines two-step inertia
Externí odkaz:
http://arxiv.org/abs/2306.07614
Autor:
Guo, Chenzheng, Zhao, Jing
In the paper, we introduce several accelerate iterative algorithms for solving the multiple-set split common fixed-point problem of quasi-nonexpansive operators in real Hilbert space. Based on primal-dual method, we construct several iterative algori
Externí odkaz:
http://arxiv.org/abs/2306.04208
Machine learning develops rapidly, which has made many theoretical breakthroughs and is widely applied in various fields. Optimization, as an important part of machine learning, has attracted much attention of researchers. With the exponential growth
Externí odkaz:
http://arxiv.org/abs/1906.06821