检索结果-南通市图书馆

基于admm的多场景县域多微电网交互运行策略

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

中国电力 2023年

作者：张冲标钱辰雯俞红燕彭燕玲陈金威嘉善恒兴电力建设有限公司国网浙江省电力有限公司嘉善县供电公司

在“双碳“目标的引领下，可再生能源的利用及消纳需求已经迎来快速增长。为了实现县域内不同负荷特性的微电网可再生能源优势互补，提高消纳率，提出了基于admm的多场景县域多微电网交互运行策略；首先，建立微电网独立运行的调度模型... 详细信息

在“双碳“目标的引领下，可再生能源的利用及消纳需求已经迎来快速增长。为了实现县域内不同负荷特性的微电网可再生能源优势互补，提高消纳率，提出了基于admm的多场景县域多微电网交互运行策略；首先，建立微电网独立运行的调度模型，在日前预调度计划中实现最优调度策略；其次，建立多微电网电能交互运行模型，利用admm算法迭代求解各微网间全局交互电量；最后，利用Shapley值法分配微网群系统的收益，降低每个子微网的系统运行成本；算例分析表明，所提方法不仅能提高可再生能源的消纳率，实现经济性还能降低碳排放量，实现低碳运行。

关键词：多微电网多场景阶梯式碳交易 admm Shapley

基于admm与GSP方法的产消者群日前-实时两阶段电能交易方法

在线全文

同方期刊数据库

学校读者我要写书评

暂无评论

电力建设 2023年

作者：李建立黄显斌李勇高红均曾晓璞单俊嘉刘青雨国网天府新区供电公司四川大学电气工程学院

随着具备分布式资源的产消者群逐渐成为新兴电力市场主体，分布式资源如何从仅满足产消者群自给自足到进一步有效利用成为重要命题，首先分析分布式电能发展现状，针对产消者群提出一种分布式交易框架，在此框架下，根据产消者群实际情... 详细信息

随着具备分布式资源的产消者群逐渐成为新兴电力市场主体，分布式资源如何从仅满足产消者群自给自足到进一步有效利用成为重要命题，首先分析分布式电能发展现状，针对产消者群提出一种分布式交易框架，在此框架下，根据产消者群实际情况建立了产消者群模型，并制定日前-实时两阶段电能交易方法。在日前阶段，基于交替方向乘子法（Alternating Direction Method of Multipliers，admm）实现产消者群的日前电能交易，通过电能供需关系迭代拉格朗日乘子得到交易价格，既解决了产消者群议价难题也提升了可再生资源利用率；接着，在实时阶段，基于广义二价拍卖（Generalized Second-Price，GSP）规则使产消者群在电能拍卖中合理出价，既解决了“光伏发电容易受到天气影响及产消者自身负荷预测准确度低导致电能偏差”的问题，也满足了实时电能交易中多产消者群之间对交易时限的要求，最后，通过算例验证了本文方法的合理性。

关键词：分布式交易框架产消者群 GSP admm 可再生资源

Beamforming Optimization for RIS-Aided SWIPT in Cell-Free MIMO Networks

在线全文

同方期刊数据库

学校读者我要写书评

暂无评论

China Communications 2021年第9期18卷 175-191页

作者： Ziyi Yang Yu Zhang School of Information and Electronics Beijing Institute of TechnologyBeijing 100086China

Reconfigurable intelligent surface(RIS)is a promising technology that utilizes massive low-cost elements to reflect incident signals to desired direction.In this paper,we consider a RIS-aided cell free multiple-input multiple-output(MIMO)network that composed of multiple access points(APs),multiple RISs,multiple information receivers(IRs),and multiple energy receivers(ERs).Under the maximum transmit power constraints of the APs and the minimum total energy harvesting power constraints of the ERs,we aim to maximize the weighted sum-rate(WSR)by jointly optimizing the beamforming matrices and the passive RIS reflection matrices.However,the formulated problem is nonconvex and very challenging to solve.Therefore,we propose an alternating optimization algorithm to obtain a suboptimal solution.Explicitly,the proposed algorithm decomposes the original optimization problem into three subproblems that can be efficiently solved.To overcome the nonconvexity of the subproblems,a successive convex approximation(SCA)based algorithm is proposed to tackle the energy harvesting constraints.Moreover,an alternating direction method of multipliers(admm)based algorithm is proposed to optimize the constant modulus constrained RIS reflecting elements.Finally,simulations are conducted to demonstrate the performance advantages of the proposed algorithms.

关键词： cell-free networks SWIPT Reconfigurable intelligent surface SCA admm

维普期刊数据库

一种使用Bi-admm优化深度学习模型的方案

在线全文

学校读者我要写书评

暂无评论

信息网络安全 2023年第2期23卷 54-63页

作者：徐占洋程洛飞程建春许小龙南京信息工程大学软件学院南京210044

admm算法被广泛应用于传统机器学习模型优化领域,它解决了某些深度学习的优化问题。该算法在优化深度学习模型方面的表现已经超过了大多数基于梯度的优化算法,而Bi-admm算法比admm算法的收敛速度更快、更稳定。文章提出了一种优化深度... 详细信息

admm算法被广泛应用于传统机器学习模型优化领域,它解决了某些深度学习的优化问题。该算法在优化深度学习模型方面的表现已经超过了大多数基于梯度的优化算法,而Bi-admm算法比admm算法的收敛速度更快、更稳定。文章提出了一种优化深度学习模型方案dlBi-admm算法,并用该算法来训练深度学习模型。首先,文章采用加速近端梯度算法优化耦合变量来降低矩阵求逆运算的复杂度;然后,详细给出每个变量的优化子问题的具体函数;最后,通过实验证明文章所提dlBiadmm算法优化的结果比dladmm优化的结果更能提高模型的精度,且dlBi-admm算法比dladmm算法在时间效率上表现更好。

关键词：深度学习 admm dladmm Bi-admm 加速近端梯度算法

维普期刊数据库

MEC-Assisted Flexible Transcoding Strategy for Adaptive Bitrate Video Streaming in Small Cell Networks

在线全文

学校读者我要写书评

暂无评论

China Communications 2021年第2期18卷 200-214页

作者： Chunyu Liu Heli Zhang Hong Ji Xi Li School of Information and Communication Engineering Beijing University of Posts and TelecommunicationsBeijing 100876China Key Laboratory of Universal Wireless Communications Ministry of Education(Beijing University of Posts and Telecommunications)Beijing 100876China

Adaptive bitrate video streaming(ABR)has become a critical technique for mobile video streaming to cope with time-varying network conditions and different user preferences.However,there are still many problems in achieving high-quality ABR video streaming over cellular networks.Mobile Edge Computing(MEC)is a promising paradigm to overcome the above problems by providing video transcoding capability and caching the ABR video streaming within the radio access network(RAN).In this paper,we propose a flexible transcoding strategy to provide viewers with low-latency video streaming services in the MEC networks under the limited storage,computing,and spectrum resources.According to the information collected from users,the MEC server acts as a controlling component to adjust the transcoding strategy flexibly based on optimizing the video caching placement strategy.Specifically,we cache the proper bitrate version of the video segments at the edge servers and select the appropriate bitrate version of the video segments to perform transcoding under jointly considering access control,resource allocation,and user preferences.We formulate this problem as a nonconvex optimization and mixed combinatorial problem.Moreover,the simulation results indicate that our proposed algorithm can ensure a low-latency viewing experience for users.

关键词： mobile edge computing adaptive bi-trate video streaming flexible transcoding strategy admm

维普期刊数据库

First-Order Algorithms for Convex Optimization with Nonseparable Objective and Coupled Constraints

在线全文

学校读者我要写书评

暂无评论

Journal of the Operations Research Society of China 2017年第2期5卷 131-159页

作者： Xiang Gao Shu-Zhong Zhang Department of Industrial and Systems Engineering University of MinnesotaMinneapolisMN 55455USA

In this paper,we consider a block-structured convex optimization model,where in the objective the block variables are nonseparable and they are further linearly coupled in the constraint.For the 2-block case,we propose a number of first-order algorithms to solve this model.First,the alternating direction method of multipliers(admm)is extended,assuming that it is easy to optimize the augmented Lagrangian function with one block of variables at each time while fixing the other block.We prove that O(1/t)iteration complexity bound holds under suitable conditions,where t is the number of iterations.If the subroutines of the admm cannot be implemented,then we propose new alternative algorithms to be called alternating proximal gradient method of multipliers,alternating gradient projection method of multipliers,and the hybrids thereof.Under suitable conditions,the O(1/t)iteration complexity bound is shown to hold for all the newly proposed algorithms.Finally,we extend the analysis for the admm to the general multi-block case.

关键词： First-order algorithms admm Proximal gradient method Convex optimization Iteration complexity

ENHANCED BLOCK-SPARSE SIGNAL RECOVERY PERFORMANCE VIA TRUNCATED l2\l1-2 MINIMIZATION

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Journal of Computational Mathematics 2020年第3期38卷 437-451页

作者： Weichao Kong Jianjun Wang Wendong Wang Feng Zhang School of Mathematics and Statistics Southwest UniversityChongqing 400715China Research Center for Artificial Intelligence Education Big Data Southwest UniversityChongqing 400715China

In this paper,we investigate truncated l2\l1-2 minimization and its associated alternating direction method of multipliers(admm)algorithm for recovering the block sparse signals.Based on the block restricted isometry property(Block-RIP),a theoretical analysis is presen ted to guarantee the validity of proposed method.Our theore tical resul ts not only show a less error upper bound,but also promote the former recovery condition of truncated l1-2 method for sparse signal recovery.Besides,the algorithm has been compared with some state-of-the-art algorithms and numerical experiments have shown excellent performances on recovering the block sparse signals.

关键词： Compressed sensing Block-sparse Trunca ted l2\l1-2 minimization met hod admm

PROXIMAL-PROXIMAL-GRADIENT METHOD

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Journal of Computational Mathematics 2019年第6期37卷 778-812页

作者： Ernest K.Ryu Wotao Yin Department of Mathematics University of CaliforniaLos AngelesCA 90095USA

In this paper,we present the proximal-proximal-gradient method(PPG),a novel optimization method that is simple to implement and simple to parallelize.PPG generalizes the proximal-gradient method and admm and is applicable to minimization problems written as a sum of many differentiable and many non-differentiable convex functions.The non-differentiable functions can be coupled.We furthermore present a related stochastic variation,which we call stochastic PPG(S-PPG).S-PPG can be interpreted as a generalization of Finito and MISO over to the sum of many coupled non-differentiable convex functions.We present many applications that can benefit from PPG and S-PPG and prove convergence for both methods.We demonstrate the empirical effectiveness of both methods through experiments on a CUDA GPU.A key strength of PPG and S-PPG is,compared to existing methods,their ability to directly handle a large sum of non-differentiable nonseparable functions with a constant stepsize independent of the number of functions.Such non-diminishing stepsizes allows them to be fast.

关键词： Proximal-gradient admm Finito MISO SAGA Operator splitting Firstorder methods Distributed optimization Stochastic optimization Almost sure convergence linear convergence

REDUCED-RANK MODELING FOR HIGH-DIMENSIONAL MODEL-BASED CLUSTERING

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Journal of Computational Mathematics 2018年第3期36卷 426-440页

作者： Lei Yang Junhui Wang Shiqian Ma Department of Environmental Medicine New York University New York NY USA Department of Mathematics City University of Hong Kong Kowloon Tong Hong Kong Department of Mathematics University of California Davis CA 95616 USA

Model-based clustering is popularly used in statistical literature, which often models the data with a Gaussian mixture model. As a consequence, it requires estimation of a large amount of parameters, especially when the data dimension is relatively large, in this paper, reduced-rank model and group-sparsity regularization are proposed to equip with the model-based clustering, which substantially reduce the number of parameters and thus facilitate the high-dimensional clustering and variable selection simultaneously. We propose an EM algorithm for this task, in which the M-step is solved using alternating minimization. One of the alternating steps involves both nonsmooth function and nonconvex constraint, and thus we propose a linearized alternating direction method of multipliers （admm） for solving it. This leads to an efficient algorithm whose subproblems are all easy to solve. In addition, a model selection criterion based on the concept of clustering stability is developed for tuning the clustering model. The effectiveness of the proposed method is supported in a variety of simulated and real examples, as well as its asymptotic estimation and selection consistencies.

关键词： Clustering Gaussian mixture model Group Lasso admm Reduced-rankmodel