Výsledky vyhledávání - Approximation proximal gradient algorithm

An approximation proximal gradient algorithm for nonconvex-linear minimax problems with nonconvex nonsmooth terms Autor He, Jiefei, Zhang, Huiling, Xu, Zi

“… In this paper, we propose an efficient approximation proximal gradient algorithm for solving a class of nonsmooth nonconvex-linear minimax problems with a nonconvex nonsmooth term, and the number…”
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Stochastic proximal-gradient algorithms for penalized mixed models Autor Fort, Gersende, Ollier, Edouard, Samson, Adeline

“… can be approximated by a Monte Carlo technique. We propose a new algorithm based on a stochastic approximation of the proximal-gradient (PG) algorithm…”
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Fast nonnegative tensor factorization based on accelerated proximal gradient and low-rank approximation Autor Zhang, Yu, Zhou, Guoxu, Zhao, Qibin, Cichocki, Andrzej, Wang, Xingyu

“… In this study, we propose a new algorithm called FastNTF_APG to speed up NTF by combining accelerated proximal gradient and low-rank approximation…”
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Smoothing composite proximal gradient algorithm for sparse group Lasso problems with nonsmooth loss functions Autor Shen, Huiling, Peng, Dingtao, Zhang, Xian

“… Then, based on the smooth approximation of the loss function, a smoothing composite proximal gradient (SCPG…”
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New inertial proximal gradient methods for unconstrained convex optimization problems Autor Duan, Peichao, Zhang, Yiqun, Bu, Qinxiong

“… In this paper, firstly, we propose inexact inertial acceleration methods based on the viscosity approximation and proximal scaled gradient algorithm to accelerate the convergence of the algorithm…”
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A dual active-set proximal Newton algorithm for sparse approximation of correlation matrices Autor Liu, Xiao, Shen, Chungen, Wang, Li

“…In this paper, we propose a novel dual active-set algorithm that is based on proximal gradient and semi-smooth Newton iterations for the sparse approximation of correlation matrices in the Frobenius norm…”
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Automatic motion capture data denoising via filtered subspace clustering and low rank matrix approximation Autor Liu, Xin, Cheung, Yiu-ming, Peng, Shu-Juan, Cui, Zhen, Zhong, Bineng, Du, Ji-Xiang

“… piecewise motion always share the similar low dimensional subspace representation. Then, we employ the accelerated proximal gradient (APG…”
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Decentralized Proximal Gradient Algorithms With Linear Convergence Rates Autor Alghunaim, Sulaiman A., Ryu, Ernest K., Yuan, Kun, Sayed, Ali H.

“…) for the class of algorithms that uses the gradients and the proximal mappings of the smooth and nonsmooth parts, respectively…”
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Convergence Rate Analysis of Viscosity Approximation based Gradient Algorithms Autor Jain, Prayas, Verma, Mridula, Shukla, K.K

“… In this paper, we present a viscosity-approximation-based proximal gradient algorithm and prove its linear convergence rate…”
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Alternated and multi-step inertial approximation methods for solving convex bilevel optimization problems Autor Duan, Peichao, Zhang, Yiqun

“…In this paper, we propose three kinds of inertial approximation methods based on the proximal gradient algorithm to accelerate the convergence of the algorithm for solving convex bilevel optimization problems…”
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Unmatched Preconditioning of the Proximal Gradient Algorithm Autor Savanier, Marion, Chouzenoux, Emilie, Pesquet, Jean-Christophe, Riddell, Cyril

“…This work addresses the resolution of penalized least-squares problems using the proximal gradient algorithm (PGA…”
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A successive difference-of-convex approximation method for a class of nonconvex nonsmooth optimization problems Autor Liu, Tianxiang, Pong, Ting Kei, Takeda, Akiko

“…: the corresponding proximal mapping can be hard to compute so that standard first-order methods such as the proximal gradient algorithm cannot be applied efficiently…”
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A Fast Proximal Gradient Algorithm to Solve Electromagnetic Inverse Scattering Problems Autor Ma, Zhenchao, Xu, Kuiwen

“…). In this paper, we propose three methods to solve it using the proximal gradient algorithm (PGA) based on the Born approximation…”
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Improved Step-Size Schedules for Proximal Noisy Gradient Methods Autor Khirirat, Sarit, Wang, Xiaoyu, Magnusson, Sindri, Johansson, Mikael

“…Noisy gradient algorithms have emerged as one of the most popular algorithms for distributed optimization with massive data…”
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Decentralized Inexact Proximal Gradient Method With Network-Independent Stepsizes for Convex Composite Optimization Autor Guo, Luyao, Shi, Xinli, Cao, Jinde, Wang, Zihao

“…This paper proposes a novel CTA (Combine-Then-Adapt)-based decentralized algorithm for solving convex composite optimization problems over undirected and connected networks…”
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Convergence of Stochastic Proximal Gradient Algorithm Autor Rosasco, Lorenzo, Villa, Silvia, Vũ, Bằng Công

“…We study the extension of the proximal gradient algorithm where only a stochastic gradient estimate is available and a relaxation step is allowed…”
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A smoothing proximal gradient algorithm with extrapolation for the relaxation of ℓ0 regularization problem Autor Zhang, Jie, Yang, Xinmin, Li, Gaoxi, Zhang, Ke

“… (SIAM J Numer Anal 58:858–883, 2020) and propose a smoothing proximal gradient algorithm with extrapolation (SPGE…”
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A smoothing proximal gradient algorithm for matrix rank minimization problem Autor Yu, Quan, Zhang, Xinzhen

“… Then the smoothing proximal gradient (SPG) algorithm is proposed to find a lifted stationary point of the continuous relaxation model. Moreover, it is shown that any accumulating point of the sequence generated by SPG algorithm is a lifted stationary point. At last, numerical examples show the efficiency of the SPG algorithm…”
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A Proximal–Based Algorithm for Piecewise Sparse Approximation with Application to Scattered Data Fitting Autor Zhong, Yijun, Li, Chongjun, Li, Zhong, Duan, Xiaojuan

“… In this paper, we propose a piecewise sparse approximation model and a piecewise proximal gradient method (JPGA…”
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Training Compact DNNs with ℓ1/2 Regularization Autor Tang, Anda, Niu, Lingfeng, Miao, Jianyu, Zhang, Peng

“…•We give a closed-form, threshold solution to the proximal operator of ℓ1/2, and consequently design a stochastic proximal gradient algorithm to train the resulting model…”
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