Výsledky vyhledávání - Alternating randomized proximal gradient algorithm

Zeroth-order single-loop algorithms for nonconvex-linear minimax problems Autor Shen, Jingjing, Wang, Ziqi, Xu, Zi

“… . Furthermore, we propose a zeroth-order alternating randomized proximal gradient algorithm for block-wise nonsmooth nonconvex-linear minimax problems and its corresponding iteration complexity is O K 3 2 ε…”
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Variational Analysis Perspective on Linear Convergence of Some First Order Methods for Nonsmooth Convex Optimization Problems Autor Ye, Jane J., Yuan, Xiaoming, Zeng, Shangzhi, Zhang, Jin

“…), the proximal alternating linearized minimization (PALM) algorithm and the randomized block coordinate proximal gradient method (R-BCPGM…”
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Smoothing randomized block-coordinate proximal gradient algorithms for nonsmooth nonconvex composite optimization Autor Li, Xue, Bian, Wei

“…) algorithm and a Bregman randomized block-coordinate proximal gradient (B-RBCPG) algorithm for minimizing the sum of two nonconvex nonsmooth functions, one of which is block separable…”
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A Distributed Proximal Alternating Direction Multiplier Method for Multiblock Nonsmooth Composite Optimization Autor Zhou, Yuan, Guo, Luyao, Shi, Xinli, Cao, Jinde

“… To tackle this problem, we propose a novel distributed proximal alternating direction multiplier method (ADMM…”
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Derivative-free Alternating Projection Algorithms for General Nonconvex-Concave Minimax Problems Autor Xu, Zi, Wang, Ziqi, Shen, Jingjing, Dai, Yuhong

“… Moreover, we propose a zeroth-order block alternating randomized proximal gradient algorithm (ZO-BAPG…”
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Inertial accelerated stochastic mirror descent for large-scale generalized tensor CP decomposition Autor Liu, Zehui, Wang, Qingsong, Cui, Chunfeng, Xia, Yong

“… This paper explores generalized tensor CP decomposition, employing the Bregman distance as the proximal term and introducing an inertial accelerated block randomized stochastic mirror descent algorithm (iTableSMD…”
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Random block-coordinate methods for inconsistent convex optimisation problems Autor Staudigl, Mathias, Jacquot, Paulin

“… Lying midway between the celebrated Chambolle–Pock primal-dual algorithm and Tseng’s accelerated proximal gradient method, we establish global convergence of the last iterate as well as optimal O ( 1 / k ) and O ( 1 / k 2…”
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Low-order Optimization Algorithms: Iteration Complexity and Applications Autor Gao, Xiang

“…Efficiency and scalability have become the new norms to evaluate optimization algorithms in the modern era of big data analytics…”
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Adaptive Catalyst for Smooth Convex Optimization Autor Ivanova, Anastasiya, Pasechnyuk, Dmitry, Grishchenko, Dmitry, Shulgin, Egor, Gasnikov, Alexander, Matyukhin, Vladislav

“…In this paper, we present a generic framework that allows accelerating almost arbitrary non-accelerated deterministic and randomized algorithms for smooth convex optimization problems…”
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Efficiency of Coordinate Descent Methods For Structured Nonconvex Optimization Autor Deng, Qi, Lan, Chenghao

“… First, by extending randomized CD to nonsmooth nonconvex settings, we develop a coordinate subgradient method that randomly updates block-coordinate variables by using block composite subgradient mapping…”
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