Suchergebnisse - "Proximal-gradient algorithm"

A recent proximal gradient algorithm for convex minimization problem using double inertial extrapolations von Kesornprom, Suparat, Inkrong, Papatsara, Witthayarat, Uamporn, Cholamjiak, Prasit

“… In this study, we suggest a new class of forward-backward (FB) algorithms designed to solve convex minimization problems. Our method incorporates a linesearch …”
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An inexact continuation accelerated proximal gradient algorithm for low n-rank tensor recovery von Liu, Huihui, Song, Zhanjie

“… Furthermore, in order to solve the unconstrained nonsmooth convex optimization problem, an accelerated proximal gradient algorithm is proposed …”
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Identifying Heterogeneous Effect Using Latent Supervised Clustering With Adaptive Fusion von Chen, Jingxiang, Tran-Dinh, Quoc, Kosorok, Michael R., Liu, Yufeng

“… In particular, we formulate the problem as a regression problem with subject specific coefficients, and use adaptive fusion to cluster the coefficients into subpopulations …”
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Incremental proximal gradient scheme with penalization for constrained composite convex optimization problems von Petrot, Narin, Nimana, Nimit

“… We consider the problem of minimizing a finite sum of convex functions subject to the set of minimizers of a convex differentiable function …”
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An Inertial Proximal-Gradient Penalization Scheme for Constrained Convex Optimization Problems von Boţ, Radu Ioan, Csetnek, Ernö Robert, Nimana, Nimit

“… We propose a proximal-gradient algorithm with penalization terms and inertial and memory effects for minimizing the sum of a proper, convex, and lower semicontinuous and a convex differentiable …”
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Stochastic proximal gradient methods for nonconvex problems in Hilbert spaces von Geiersbach, Caroline, Scarinci, Teresa

“… For finite-dimensional problems, stochastic approximation methods have long been used to solve stochastic optimization problems. Their application to …”
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Cauchy non-convex sparse feature selection method for the high-dimensional small-sample problem in motor imagery EEG decoding von Zhang, Shaorong, Wang, Qihui, Zhang, Benxin, Liang, Zhen, Zhang, Li, Li, Linling, Huang, Gan, Zhang, Zhiguo, Feng, Bao, Yu, Tianyou

“… By designing a proximal gradient algorithm, our proposed model achieves closer-to-unbiased estimation than existing models …”
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An inexact quasi-Newton algorithm for large-scale ℓ1 optimization with box constraints von Cheng, Wanyou, LinPeng, Zhuanghan, Li, Donghui

“… The algorithm uses the identification technique of the proximal gradient algorithm to estimate the active set and free variables …”
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Non-signal components minimization for sparse signal recovery von Xiang, Min, Zhang, Zhenyue

“… Additionally, an improved accelerated proximal gradient algorithm is provided to solve each non-signal components minimization problem penalized by the residual …”
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Accelerated Graph Learning From Smooth Signals von Saboksayr, Seyed Saman, Mateos, Gonzalo

“… A fast dual-based proximal gradient algorithm is developed to efficiently tackle a strongly convex, smoothness-regularized network inverse problem known to yield high-quality graph solutions …”
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A modified inertial proximal gradient method for minimization problems and applications von Kesornprom, Suparat, Cholamjiak, Prasit

“… In this paper, the aim is to design a new proximal gradient algorithm by using the inertial technique with adaptive stepsize for solving convex minimization problems and prove convergence …”
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DISTRIBUTED PROXIMAL-GRADIENT METHOD FOR CONVEX OPTIMIZATION WITH INEQUALITY CONSTRAINTS von LI, JUEYOU, WU, CHANGZHI, WU, ZHIYOU, LONG, QIANG, WANG, XIANGYU

“… -gradient algorithm over a time-changing connectivity network, and establish a convergence rate depending …”
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An interior proximal gradient method for nonconvex optimization von De Marchi, Alberto, Themelis, Andreas

“… that successfully addressed the convex case. Our interior proximal gradient algorithm benefits from warm starting, generates strictly feasible iterates with decreasing objective value, and returns after finitely many iterations a primal-dual …”
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Joint Fairness Model with Applications to Risk Predictions for Under-represented Populations von Do, Hyungrok, Nandi, Shinjini, Putzel, Preston, Smyth, Padhraic, Zhong, Judy

“… In data collection for predictive modeling, under-representation of certain groups, based on gender, race/ethnicity, or age, may yield less-accurate …”
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An interior proximal gradient method for nonconvex optimization von De Marchi, Alberto, Themelis, Andreas

“… that successfully addressed the convex case. Our interior proximal gradient algorithm benefits from warm starting, generates strictly feasible iterates with decreasing objective value, and returns after finitely many iterations a primal-dual …”
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Real-time Data-Driven Optimization Algorithms for Modern Power Systems von Ospina Sierra, Ana Maria

“… Power systems have experienced significant transformations in recent years driven by the integration of new technologies, such as renewable generation and …”
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Stochastic Proximal Gradient Methods for Nonconvex Problems in Hilbert Spaces von Geiersbach, Caroline, Scarinci, Teresa

“… For finite-dimensional problems, stochastic approximation methods have long been used to solve stochastic optimization problems. Their application to …”
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Amalgamation-Based Statistical Learning for Compositional Data von Li, Yan

“… Such data do not admit the familiar Euclidean geometry and are typically of high dimension, in ated with excessive zeros, and subject to measurement errors …”
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Machine Learning Techniques for Heterogeneous Data Sets von Chen, Jingxiang

“… Over the past few decades, machine learning tools are under rapid development in various application fields to support statistical decision making. In this …”
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Accelerated Graph Learning from Smooth Signals von Seyed Saman Saboksayr, Mateos, Gonzalo

“… A fast dual-based proximal gradient algorithm is developed to efficiently tackle a strongly convex, smoothness-regularized network inverse problem known to yield high-quality graph solutions …”
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