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Optimal Resource Allocation for Two-User and Single-DF-Relay Network With Ambient Backscatter
IEEE Access, 2019 In this paper, we investigate and analyze a two-user single decode-and-forward (DF) relay network with ambient backscatter communication capabilities, where the user nodes and the relay node are equipped with a wireless-powered device instead of embedded
Chuangming Zheng +2 more
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Robust Secure Resource Allocation for RIS-Aided SWIPT Communication Systems
Sensors, 2022 Aiming at the influence of channel uncertainty, user information leakage and harvested energy improvement, this paper proposes a robust resource allocation algorithm for reconfigurable intelligent reflector (RIS) multiple-input single-output systems ...
Bencheng Yu, Zihui Ren, Shoufeng Tang
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Distributed constrained optimization via continuous-time mirror design
Advances in Difference Equations, 2018 Recently, distributed convex optimization using a multiagent system has received much attention by many researchers. This problem is frequently approached by combing the consensus algorithms in the multiagent literature and the gradient algorithms in the
Rui Sheng, Wei Ni
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Erratum to: On convex optimization without convex representation [PDF]
Optimization Letters, 2014 The proof of Theorem 3 in the original publication of the article contains an incorrect statement that we fix below. Theorem 3 Let K in (1.2) be compact and let Assumption 1 hold true. For every fixed μ > 0, choose xμ ∈ K to be an arbitrary stationary point of φμ in K.
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Tsinghua Science and Technology, 2017 We consider a wide range of non-convex regularized minimization problems, where the non-convex regularization term is composite with a linear function engaged in sparse learning.
Linbo Qiao +3 more
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Are Convex Optimization Curves Convex?
CoRRIn this paper, we study when we might expect the optimization curve induced by gradient descent to be \emph{convex} -- precluding, for example, an initial plateau followed by a sharp decrease, making it difficult to decide when optimization should stop.
Guy Barzilai, Ohad Shamir, Moslem Zamani
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Distributed Convex Optimization with Many Convex Constraints
CoRR, 2016 We address the problem of solving convex optimization problems with many convex constraints in a distributed setting. Our approach is based on an extension of the alternating direction method of multipliers (ADMM) that recently gained a lot of attention in the Big Data context.
Joachim Giesen, Sören Laue
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Beyond Convexity: Stochastic Quasi-Convex Optimization
CoRR, 2015 Stochastic convex optimization is a basic and well studied primitive in machine learning. It is well known that convex and Lipschitz functions can be minimized efficiently using Stochastic Gradient Descent (SGD). The Normalized Gradient Descent (NGD) algorithm, is an adaptation of Gradient Descent, which updates according to the direction of the ...
Elad Hazan +2 more
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Large Scale Resource Allocation for the Internet of Things Network Based on ADMM
IEEE Access, 2020 Large scale deployment of Internet of Things (IoT) devices poses challenges in resource allocation. In this paper, alternating direction method of multipliers (ADMM) is adopted to solve such large scale resource allocation problems.
Yanhua He +3 more
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Joint User Association and Beamforming Design for ISAC Networks With Large Language Models
IEEE Open Journal of the Communications SocietyIntegrated sensing and communication (ISAC) has been envisioned to play a more important role in future wireless networks. However, the design of ISAC networks is challenging, especially when there are multiple communication and sensing (C&S ...
Haoyun Li +5 more
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