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检索条件"主题词=non-convex and non-smooth optimization"
7 条 记 录,以下是1-10 订阅
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Nested Alternating Minimization with FISTA for non-convex and non-smooth optimization Problems
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JOURNAL OF optimization THEORY AND APPLICATIONS 2023年 第3期199卷 1130-1157页
作者: Gur, Eyal Sabach, Shoham Shtern, Shimrit Technion Israel Inst Technol Fac Data & Decis Sci IL-320003 Hefa Israel
Motivated by a recent framework for proving global convergence to critical points of nested alternating minimization algorithms, which was proposed for the case of smooth subproblems, we first show here that non-smoot... 详细信息
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Almost sure convergence of stochastic composite objective mirror descent for non-convex non-smooth optimization
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optimization LETTERS 2024年 第9期18卷 2113-2131页
作者: Liang, Yuqing Xu, Dongpo Zhang, Naimin Mandic, Danilo P. Northeast Normal Univ Sch Math & Stat Key Lab Appl Stat MOE Changchun 130024 Peoples R China Wenzhou Univ Coll Math & Phys Wenzhou 325035 Peoples R China Imperial Coll London Dept Elect & Elect Engn London SW7 2AZ England
Stochastic composite objective mirror descent (SCOMID) is an effective method for solving large-scale stochastic composite problems in machine learning. This method can efficiently use the geometric properties of a pr... 详细信息
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Distributed Stochastic Consensus optimization With Momentum for nonconvex nonsmooth Problems
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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2021年 69卷 4486-4501页
作者: Wang, Zhiguo Zhang, Jiawei Chang, Tsung-Hui Li, Jian Luo, Zhi-Quan Sichuan Univ Coll Math Chengdu 610064 Sichuan Peoples R China Chinese Univ Hong Kong Shenzhen 518172 Peoples R China Shenzhen Res Inst Big Data Shenzhen 518172 Guangdong Peoples R China Univ Florida Dept Elect & Comp Engn Gainesville FL 32611 USA
While many distributed optimization algorithms have been proposed for solving smooth or convex problems over the networks, few of them can handle non-convex and non-smooth problems. Based on a proximal primal-dual app... 详细信息
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Adaptive ADMM for Dictionary Learning in Convolutional Sparse Representation
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IEEE TRANSACTIONS ON IMAGE PROCESSING 2019年 第7期28卷 3408-3422页
作者: Peng, Guan-Ju Natl Chung Hsing Univ Dept Appl Math Taichung 402 Taiwan
In this paper, we propose a novel approach to convolutional sparse representation with the aim of resolving the dictionary learning problem. The proposed method, referred to as the adaptive alternating direction metho... 详细信息
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Robust Networked Federated Learning for Localization
Robust Networked Federated Learning for Localization
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Asia-Pacific-Signal-and-Information-Processing-Association Annual Summit and Conference (APSIPA ASC)
作者: Mirzaeifard, Reza Venkategowda, Naveen K. D. Werner, Stefan Norwegian Univ Sci & Technol NTNU Dept Elect Syst Trondheim Norway Linkoping Univ Dept Sci & Technol Linkoping Sweden
This paper addresses the problem of localization, which is inherently non-convex and non-smooth in a federated setting where the data is distributed across a multitude of devices. Due to the decentralized nature of fe... 详细信息
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Moreau Envelope ADMM for Decentralized Weakly convex optimization
Moreau Envelope ADMM for Decentralized Weakly Convex Optimiz...
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Asia-Pacific-Signal-and-Information-Processing-Association Annual Summit and Conference (APSIPA ASC)
作者: Mirzaeifard, Reza Venkategowda, Naveen K. D. Jung, Alexander Werner, Stefan Norwegian Univ Sci & Technol Dept Elect Syst Trondheim Norway Linkoping Univ Dept Sci & Technol Linkoping Sweden Aalto Univ Dept Comp Sci Espoo Finland
This paper proposes a proximal variant of the alternating direction method of multipliers (ADMM) for distributed optimization. Although the current versions of ADMM algorithm provide promising numerical results in pro... 详细信息
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Robust Phase Retrieval with non-convex Penalties  56
Robust Phase Retrieval with Non-Convex Penalties
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56th Asilomar Conference on Signals, Systems, and Computers
作者: Mirzaeifard, Reza Venkategowda, Naveen K. D. Werner, Stefan Norwegian Univ Sci & Technol NTNU Dept Elect Syst Trondheim Norway Linkoping Univ Dept Sci & Technol Linkoping Sweden
This paper proposes an alternating direction method of multiplier (ADMM) based algorithm for solving the sparse robust phase retrieval with non-convex and non-smooth sparse penalties, such as minimax concave penalty (... 详细信息
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