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Distributionally robust optimization based chance-constrained energy management for hybrid energy powered cellular networks 被引量:1
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作者 Pengfei Du Hongjiang Lei +2 位作者 Imran Shafique Ansari Jianbo Du Xiaoli Chu 《Digital Communications and Networks》 SCIE CSCD 2023年第3期797-808,共12页
Energy harvesting has been recognized as a promising technique with which to effectively reduce carbon emis-sions and electricity expenses of base stations.However,renewable energy is inherently stochastic and inter-m... Energy harvesting has been recognized as a promising technique with which to effectively reduce carbon emis-sions and electricity expenses of base stations.However,renewable energy is inherently stochastic and inter-mittent,imposing formidable challenges on reliably satisfying users'time-varying wireless traffic demands.In addition,the probability distribution of the renewable energy or users’wireless traffic demand is not always fully known in practice.In this paper,we minimize the total energy cost of a hybrid-energy-powered cellular network by jointly optimizing the energy sharing among base stations,the battery charging and discharging rates,and the energy purchased from the grid under the constraint of a limited battery size at each base station.In solving the formulated non-convex chance-constrained stochastic optimization problem,a new ambiguity set is built to characterize the uncertainties in the renewable energy and wireless traffic demands according to interval sets of the mean and covariance.Using this ambiguity set,the original optimization problem is transformed into a more tractable second-order cone programming problem by exploiting the distributionally robust optimization approach.Furthermore,a low-complexity distributionally robust chance-constrained energy management algo-rithm,which requires only interval sets of the mean and covariance of stochastic parameters,is proposed.The results of extensive simulation are presented to demonstrate that the proposed algorithm outperforms existing methods in terms of the computational complexity,energy cost,and reliability. 展开更多
关键词 Cellular networks Energy harvesting Energy management Chance-constrained distributionally robust optimization
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Distributionally Robust Newsvendor Model for Fresh Products under Cap-and-Offset Regulation
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作者 Xuan Zhao Jianteng Xu Hongling Lu 《Computer Modeling in Engineering & Sciences》 SCIE EI 2023年第8期1813-1833,共21页
The cap-and-offset regulation is a practical scheme to lessen carbon emissions.The retailer selling fresh products can adopt sustainable technologies to lessen greenhouse gas emissions.We aim to analyze the optimal jo... The cap-and-offset regulation is a practical scheme to lessen carbon emissions.The retailer selling fresh products can adopt sustainable technologies to lessen greenhouse gas emissions.We aim to analyze the optimal joint strategies on order quantity and sustainable technology investment when the retailer faces stochastic market demand and can only acquire the mean and variance of distribution information.We construct a distributionally robust optimization model and use the Karush-Kuhn-Tucker(KKT)conditions to solve the analytic formula of optimal solutions.By comparing the models with and without investing in sustainable technologies,we examine the effect of sustainable technologies on the operational management decisions of the retailer.Finally,some computational examples are applied to analyze the impact of critical factors on operational strategies,and some managerial insights are given based on the analysis results. 展开更多
关键词 distributionally robust optimization KKT conditions cap-and-offset regulation fresh products
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Distributed Stochastic Optimization with Compression for Non-Strongly Convex Objectives
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作者 Xuanjie Li Yuedong Xu 《Computer Modeling in Engineering & Sciences》 SCIE EI 2024年第4期459-481,共23页
We are investigating the distributed optimization problem,where a network of nodes works together to minimize a global objective that is a finite sum of their stored local functions.Since nodes exchange optimization p... We are investigating the distributed optimization problem,where a network of nodes works together to minimize a global objective that is a finite sum of their stored local functions.Since nodes exchange optimization parameters through the wireless network,large-scale training models can create communication bottlenecks,resulting in slower training times.To address this issue,CHOCO-SGD was proposed,which allows compressing information with arbitrary precision without reducing the convergence rate for strongly convex objective functions.Nevertheless,most convex functions are not strongly convex(such as logistic regression or Lasso),which raises the question of whether this algorithm can be applied to non-strongly convex functions.In this paper,we provide the first theoretical analysis of the convergence rate of CHOCO-SGD on non-strongly convex objectives.We derive a sufficient condition,which limits the fidelity of compression,to guarantee convergence.Moreover,our analysis demonstrates that within the fidelity threshold,this algorithm can significantly reduce transmission burden while maintaining the same convergence rate order as its no-compression equivalent.Numerical experiments further validate the theoretical findings by demonstrating that CHOCO-SGD improves communication efficiency and keeps the same convergence rate order simultaneously.And experiments also show that the algorithm fails to converge with low compression fidelity and in time-varying topologies.Overall,our study offers valuable insights into the potential applicability of CHOCO-SGD for non-strongly convex objectives.Additionally,we provide practical guidelines for researchers seeking to utilize this algorithm in real-world scenarios. 展开更多
关键词 Distributed stochastic optimization arbitrary compression fidelity non-strongly convex objective function
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Optimized Strategy for Layout of Crop Production Areas in Hunan Province
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作者 邓文 杨玉 《Agricultural Science & Technology》 CAS 2014年第11期2049-2052,共4页
The optimized strategy made a comprehensive consideration of resources, technology, market orientation, production scale, industry basis and layout based on the principle of crop security and farmers’ income increasi... The optimized strategy made a comprehensive consideration of resources, technology, market orientation, production scale, industry basis and layout based on the principle of crop security and farmers’ income increasing, and determined the general planning on layout and structure optimization of future crop production ar-eas, with present crop production, market outlook, future industry development, con-cluding crop production characteristics of the 4 crop regions, and proposing function orientation and highlights. 展开更多
关键词 Crop production Regional distribution optimized strategy Hunan
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Fully Distributed Learning for Deep Random Vector Functional-Link Networks
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作者 Huada Zhu Wu Ai 《Journal of Applied Mathematics and Physics》 2024年第4期1247-1262,共16页
In the contemporary era, the proliferation of information technology has led to an unprecedented surge in data generation, with this data being dispersed across a multitude of mobile devices. Facing these situations a... In the contemporary era, the proliferation of information technology has led to an unprecedented surge in data generation, with this data being dispersed across a multitude of mobile devices. Facing these situations and the training of deep learning model that needs great computing power support, the distributed algorithm that can carry out multi-party joint modeling has attracted everyone’s attention. The distributed training mode relieves the huge pressure of centralized model on computer computing power and communication. However, most distributed algorithms currently work in a master-slave mode, often including a central server for coordination, which to some extent will cause communication pressure, data leakage, privacy violations and other issues. To solve these problems, a decentralized fully distributed algorithm based on deep random weight neural network is proposed. The algorithm decomposes the original objective function into several sub-problems under consistency constraints, combines the decentralized average consensus (DAC) and alternating direction method of multipliers (ADMM), and achieves the goal of joint modeling and training through local calculation and communication of each node. Finally, we compare the proposed decentralized algorithm with several centralized deep neural networks with random weights, and experimental results demonstrate the effectiveness of the proposed algorithm. 展开更多
关键词 Distributed Optimization Deep Neural Network Random Vector Functional-Link (RVFL) Network Alternating Direction Method of Multipliers (ADMM)
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A Distributionally Robust Optimization Method for Passenger Flow Control Strategy and Train Scheduling on an Urban Rail Transit Line 被引量:3
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作者 Yahan Lu Lixing Yang +4 位作者 Kai Yang Ziyou Gao Housheng Zhou Fanting Meng Jianguo Qi 《Engineering》 SCIE EI CAS 2022年第5期202-220,共19页
Regular coronavirus disease 2019(COVID-19)epidemic prevention and control have raised new require-ments that necessitate operation-strategy innovation in urban rail transit.To alleviate increasingly seri-ous congestio... Regular coronavirus disease 2019(COVID-19)epidemic prevention and control have raised new require-ments that necessitate operation-strategy innovation in urban rail transit.To alleviate increasingly seri-ous congestion and further reduce the risk of cross-infection,a novel two-stage distributionally robust optimization(DRO)model is explicitly constructed,in which the probability distribution of stochastic scenarios is only partially known in advance.In the proposed model,the mean-conditional value-at-risk(CVaR)criterion is employed to obtain a tradeoff between the expected number of waiting passen-gers and the risk of congestion on an urban rail transit line.The relationship between the proposed DRO model and the traditional two-stage stochastic programming(SP)model is also depicted.Furthermore,to overcome the obstacle of model solvability resulting from imprecise probability distributions,a discrepancy-based ambiguity set is used to transform the robust counterpart into its computationally tractable form.A hybrid algorithm that combines a local search algorithm with a mixed-integer linear programming(MILP)solver is developed to improve the computational efficiency of large-scale instances.Finally,a series of numerical examples with real-world operation data are executed to validate the pro-posed approaches. 展开更多
关键词 Passenger flow control Train scheduling distributionally robust optimization Stochastic and dynamic passenger demand Ambiguity set
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CHARACTERISTCS OF FLUID FILM IN OPTIMIZED SPIRAL GROOVE MECHANICAL SEAL 被引量:8
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作者 ZHOU Jianfeng GU Boqin 《Chinese Journal of Mechanical Engineering》 SCIE EI CAS CSCD 2007年第6期54-61,共8页
In order to investigate the sealing performance variation resulted from the thermal deformation of the end faces, the equations to calculate the fluid film pressure distribution, the bearing force and the leakage rate... In order to investigate the sealing performance variation resulted from the thermal deformation of the end faces, the equations to calculate the fluid film pressure distribution, the bearing force and the leakage rate are derived, for the fluid film both in parallel gap and in wedgy gap. The geometrical parameters of the sealing members are optimized by means of heat transfer analysis and complex method. The analysis results indicate that the shallow spiral grooves can generate hydrodynamic pressure while the rotating ring rotates and the bearing force of the fluid film in spiral groove end faces is much larger than that in the flat end faces. The deformation increases the bearing force of the fluid film in flat end faces, but it decreases the hydrodynamic pressure of the fluid film in spiral groove end faces. The gap dimensions which determine the characteristics of the fluid film is obtained by coupling analysis of the frictional heat and the thermal deformation in consideration of the equilibrium condition of the bearing force and the closing force. For different gap dimensions, the relation- ship between the closing force and the leakage rate is also investigated, based on which the leakage rate can be controlled by adjusting the closing force. 展开更多
关键词 Mechanical seal Pressure distribution Optimal design Bearing force Leakage rate
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Distributionally Robust Optimal Dispatch of Virtual Power Plant Based on Moment of Renewable Energy Resource 被引量:1
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作者 Wenlu Ji YongWang +2 位作者 Xing Deng Ming Zhang Ting Ye 《Energy Engineering》 EI 2022年第5期1967-1983,共17页
Virtual power plants can effectively integrate different types of distributed energy resources,which have become a new operation mode with substantial advantages such as high flexibility,adaptability,and economy.This ... Virtual power plants can effectively integrate different types of distributed energy resources,which have become a new operation mode with substantial advantages such as high flexibility,adaptability,and economy.This paper proposes a distributionally robust optimal dispatch approach for virtual power plants to determine an optimal day-ahead dispatch under uncertainties of renewable energy sources.The proposed distributionally robust approach characterizes probability distributions of renewable power output by moments.In this regard,the faults of stochastic optimization and traditional robust optimization can be overcome.Firstly,a second-order cone-based ambiguity set that incorporates the first and second moments of renewable power output is constructed,and a day-ahead two-stage distributionally robust optimization model is proposed for virtual power plants participating in day-ahead electricity markets.Then,an effective solution method based on the affine policy and second-order cone duality theory is employed to reformulate the proposed model into a deterministic mixed-integer second-order cone programming problem,which improves the computational efficiency of the model.Finally,the numerical results demonstrate that the proposed method achieves a better balance between robustness and economy.They also validate that the dispatch strategy of virtual power plants can be adjusted to reduce costs according to the moment information of renewable power output. 展开更多
关键词 Virtual power plant optimal dispatch UNCERTAINTY distributionally robust optimization affine policy
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A novel hybrid estimation of distribution algorithm for solving hybrid flowshop scheduling problem with unrelated parallel machine 被引量:9
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作者 孙泽文 顾幸生 《Journal of Central South University》 SCIE EI CAS CSCD 2017年第8期1779-1788,共10页
The hybrid flow shop scheduling problem with unrelated parallel machine is a typical NP-hard combinatorial optimization problem, and it exists widely in chemical, manufacturing and pharmaceutical industry. In this wor... The hybrid flow shop scheduling problem with unrelated parallel machine is a typical NP-hard combinatorial optimization problem, and it exists widely in chemical, manufacturing and pharmaceutical industry. In this work, a novel mathematic model for the hybrid flow shop scheduling problem with unrelated parallel machine(HFSPUPM) was proposed. Additionally, an effective hybrid estimation of distribution algorithm was proposed to solve the HFSPUPM, taking advantage of the features in the mathematic model. In the optimization algorithm, a new individual representation method was adopted. The(EDA) structure was used for global search while the teaching learning based optimization(TLBO) strategy was used for local search. Based on the structure of the HFSPUPM, this work presents a series of discrete operations. Simulation results show the effectiveness of the proposed hybrid algorithm compared with other algorithms. 展开更多
关键词 hybrid estimation of distribution algorithm teaching learning based optimization strategy hybrid flow shop unrelated parallel machine scheduling
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Optimal distribution of reliability for a large network based on connectivity
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作者 陈玲俐 于洁 《Applied Mathematics and Mechanics(English Edition)》 SCIE EI 2008年第12期1633-1642,共10页
It is a non-polynomial complexity problem to calculate connectivity of the complex network. When the system reliability cannot be expressed as a function of element reliability, we have to apply some heuristic methods... It is a non-polynomial complexity problem to calculate connectivity of the complex network. When the system reliability cannot be expressed as a function of element reliability, we have to apply some heuristic methods for optimization based on connectivity of the network. The calculation structure of connectivity of complex network is analyzed in the paper. The coefficient matrixes of Taylor second order expansion of the system connectivity is generated based on the calculation structure of connectivity of complex network. An optimal schedule is achieved based on genetic algorithms (GA). Fitness of seeds is calculated using the Taylor expansion function of system connectivity. Precise connectivity of the optimal schedule and the Taylor expansion function of system connectivity can be achieved by the approved Minty method or the recursive decomposition algorithm. When error between approximate connectivity and the precise value exceeds the assigned value, the optimization process is continued using GA, and the Taylor function of system connectivity needs to be renewed. The optimization process is called iterative GA. Iterative GA can be used in the large network for optimal reliability attribution. One temporary optimal result will be generated every time in the iteration process. These temporary optimal results approach the real optimal results. They can be regarded as a group of approximate optimal results useful in the real project. 展开更多
关键词 optimal distribution of reliability CONNECTIVITY genetic algorithms (GA) approved Minty method recursive decomposition algorithm
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Medical Feature Selection Approach Based on Generalized Normal Distribution Algorithm
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作者 Mohamed Abdel-Basset Reda Mohamed +3 位作者 Ripon K.Chakrabortty Michael J.Ryan Yunyoung Nam Mohamed Abouhawwash 《Computers, Materials & Continua》 SCIE EI 2021年第12期2883-2901,共19页
This paper proposes a new pre-processing technique to separate the most effective features from those that might deteriorate the performance of the machine learning classifiers in terms of computational costs and clas... This paper proposes a new pre-processing technique to separate the most effective features from those that might deteriorate the performance of the machine learning classifiers in terms of computational costs and classification accuracy because of their irrelevance,redundancy,or less information;this pre-processing process is often known as feature selection.This technique is based on adopting a new optimization algorithm known as generalized normal distribution optimization(GNDO)supported by the conversion of the normal distribution to a binary one using the arctangent transfer function to convert the continuous values into binary values.Further,a novel restarting strategy(RS)is proposed to preserve the diversity among the solutions within the population by identifying the solutions that exceed a specific distance from the best-so-far and replace them with the others created using an effective updating scheme.This strategy is integrated with GNDO to propose another binary variant having a high ability to preserve the diversity of the solutions for avoiding becoming stuck in local minima and accelerating convergence,namely improved GNDO(IGNDO).The proposed GNDO and IGNDO algorithms are extensively compared with seven state-of-the-art algorithms to verify their performance on thirteen medical instances taken from the UCI repository.IGNDO is shown to be superior in terms of fitness value and classification accuracy and competitive with the others in terms of the selected features.Since the principal goal in solving the FS problem is to find the appropriate subset of features that maximize classification accuracy,IGNDO is considered the best. 展开更多
关键词 Generalized normal distribution optimization feature selection transfer function novel restarting strategy UCI repository
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THE OPTIMAL ACTIVITY DISTRIBUTION IN NONISOTHERMAL PELLETS
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作者 吴华 袁权 朱葆琳 《Chinese Journal of Chemical Engineering》 SCIE EI CAS 1985年第1期166-179,共14页
The nonisothermal effectiveness fcator for reaction with kinetics r=kc^m/(l+Kc)~a can be improved bycatalysts with nonuniform activity distribution.The optimal distribution function in one-dimensional modelwith which ... The nonisothermal effectiveness fcator for reaction with kinetics r=kc^m/(l+Kc)~a can be improved bycatalysts with nonuniform activity distribution.The optimal distribution function in one-dimensional modelwith which the effectiveness factor can be maximized is a δ-function which means that the activity of thecatalyst should be concentrated on a layer with negligible thickness in a precise locationfrom the centerof pellets.The general equations for predicting the value ofand maximum effectiveness factor as a functionof thermodynamic,kinetic and transport parameters are derived and they can be given explicitly in the case ofa=O,m=a or isothermal reaction.An active layer with definite thickness and a deviation from the optimal locationboth decrease thevalue of the effectiveness factor.It has been shown numerically that the effectiveness factor decreases slightlywith an active layer at the inner side of x but seriously at outer side. 展开更多
关键词 THE OPTIMAL ACTIVITY distribution IN NONISOTHERMAL PELLETS
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Multi-objective planning model for simultaneous reconfiguration of power distribution network and allocation of renewable energy resources and capacitors with considering uncertainties 被引量:8
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作者 Sajad Najafi Ravadanegh Mohammad Reza Jannati Oskuee Masoumeh Karimi 《Journal of Central South University》 SCIE EI CAS CSCD 2017年第8期1837-1849,共13页
This research develops a comprehensive method to solve a combinatorial problem consisting of distribution system reconfiguration, capacitor allocation, and renewable energy resources sizing and siting simultaneously a... This research develops a comprehensive method to solve a combinatorial problem consisting of distribution system reconfiguration, capacitor allocation, and renewable energy resources sizing and siting simultaneously and to improve power system's accountability and system performance parameters. Due to finding solution which is closer to realistic characteristics, load forecasting, market price errors and the uncertainties related to the variable output power of wind based DG units are put in consideration. This work employs NSGA-II accompanied by the fuzzy set theory to solve the aforementioned multi-objective problem. The proposed scheme finally leads to a solution with a minimum voltage deviation, a maximum voltage stability, lower amount of pollutant and lower cost. The cost includes the installation costs of new equipment, reconfiguration costs, power loss cost, reliability cost, cost of energy purchased from power market, upgrade costs of lines and operation and maintenance costs of DGs. Therefore, the proposed methodology improves power quality, reliability and security in lower costs besides its preserve, with the operational indices of power distribution networks in acceptable level. To validate the proposed methodology's usefulness, it was applied on the IEEE 33-bus distribution system then the outcomes were compared with initial configuration. 展开更多
关键词 optimal reconfiguration renewable energy resources sitting and sizing capacitor allocation electric distribution system uncertainty modeling scenario based-stochastic programming multi-objective genetic algorithm
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Optimizing Domain Distribution of Grain Oriented Silicon Steel by Using Antimony as the Laser Surface Alloying Element
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作者 Fengjiu SUN, Xingjie PENG and Chuanjun LI (Dept. of Physics, Northeastern University, Shengyang 110006, China) 《Journal of Materials Science & Technology》 SCIE EI CAS CSCD 2000年第2期163-164,共2页
For reducing the core loss of grain oriented silicon steel and improving its aging property, a new method, the LLSA by using Sb as the laser surface alloying element, was investigated, and at proper technique conditio... For reducing the core loss of grain oriented silicon steel and improving its aging property, a new method, the LLSA by using Sb as the laser surface alloying element, was investigated, and at proper technique conditions rather good result was obtained. 展开更多
关键词 Optimizing Domain distribution of Grain Oriented Silicon Steel by Using Antimony as the Laser Surface Alloying Element
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OPTIMAL DYNAMIC LOAD DISTRIBUTION OF MULTIPLE COOPERATING ROBOT MANIPULATORS 被引量:1
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作者 Zhao Yongsheng Huang Zhen Gao Feng(Yanshan University) 《Chinese Journal of Mechanical Engineering》 SCIE EI CAS CSCD 1994年第2期108-111,共17页
For the situation of multiple cooperating manipulators handling a single object,an equilibrium equation is presented in which the manipulator dynamics and control forces/torques are taken into account,and a expression... For the situation of multiple cooperating manipulators handling a single object,an equilibrium equation is presented in which the manipulator dynamics and control forces/torques are taken into account,and a expression is derived to allow the optimal dynamic load distribution of the combined system can be made. 展开更多
关键词 Multiple cooperating robot manipulators Optimal distribution Dynamic coordinate
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An Optimal Control-Based Distributed Reinforcement Learning Framework for A Class of Non-Convex Objective Functionals of the Multi-Agent Network 被引量:2
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作者 Zhe Chen Ning Li 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2023年第11期2081-2093,共13页
This paper studies a novel distributed optimization problem that aims to minimize the sum of the non-convex objective functionals of the multi-agent network under privacy protection, which means that the local objecti... This paper studies a novel distributed optimization problem that aims to minimize the sum of the non-convex objective functionals of the multi-agent network under privacy protection, which means that the local objective of each agent is unknown to others. The above problem involves complexity simultaneously in the time and space aspects. Yet existing works about distributed optimization mainly consider privacy protection in the space aspect where the decision variable is a vector with finite dimensions. In contrast, when the time aspect is considered in this paper, the decision variable is a continuous function concerning time. Hence, the minimization of the overall functional belongs to the calculus of variations. Traditional works usually aim to seek the optimal decision function. Due to privacy protection and non-convexity, the Euler-Lagrange equation of the proposed problem is a complicated partial differential equation.Hence, we seek the optimal decision derivative function rather than the decision function. This manner can be regarded as seeking the control input for an optimal control problem, for which we propose a centralized reinforcement learning(RL) framework. In the space aspect, we further present a distributed reinforcement learning framework to deal with the impact of privacy protection. Finally, rigorous theoretical analysis and simulation validate the effectiveness of our framework. 展开更多
关键词 Distributed optimization MULTI-AGENT optimal control reinforcement learning(RL)
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Distributed Momentum-Based Frank-Wolfe Algorithm for Stochastic Optimization 被引量:1
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作者 Jie Hou Xianlin Zeng +2 位作者 Gang Wang Jian Sun Jie Chen 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2023年第3期685-699,共15页
This paper considers distributed stochastic optimization,in which a number of agents cooperate to optimize a global objective function through local computations and information exchanges with neighbors over a network... This paper considers distributed stochastic optimization,in which a number of agents cooperate to optimize a global objective function through local computations and information exchanges with neighbors over a network.Stochastic optimization problems are usually tackled by variants of projected stochastic gradient descent.However,projecting a point onto a feasible set is often expensive.The Frank-Wolfe(FW)method has well-documented merits in handling convex constraints,but existing stochastic FW algorithms are basically developed for centralized settings.In this context,the present work puts forth a distributed stochastic Frank-Wolfe solver,by judiciously combining Nesterov's momentum and gradient tracking techniques for stochastic convex and nonconvex optimization over networks.It is shown that the convergence rate of the proposed algorithm is O(k^(-1/2))for convex optimization,and O(1/log_(2)(k))for nonconvex optimization.The efficacy of the algorithm is demonstrated by numerical simulations against a number of competing alternatives. 展开更多
关键词 Distributed optimization Frank-Wolfe(FW)algorithms momentum-based method stochastic optimization
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A dynamical neural network approach for distributionally robust chance-constrained Markov decision process 被引量:1
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作者 Tian Xia Jia Liu Zhiping Chen 《Science China Mathematics》 SCIE CSCD 2024年第6期1395-1418,共24页
In this paper,we study the distributionally robust joint chance-constrained Markov decision process.Utilizing the logarithmic transformation technique,we derive its deterministic reformulation with bi-convex terms und... In this paper,we study the distributionally robust joint chance-constrained Markov decision process.Utilizing the logarithmic transformation technique,we derive its deterministic reformulation with bi-convex terms under the moment-based uncertainty set.To cope with the non-convexity and improve the robustness of the solution,we propose a dynamical neural network approach to solve the reformulated optimization problem.Numerical results on a machine replacement problem demonstrate the efficiency of the proposed dynamical neural network approach when compared with the sequential convex approximation approach. 展开更多
关键词 Markov decision process chance constraints distributionally robust optimization moment-based ambiguity set dynamical neural network
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Fully asynchronous distributed optimization with linear convergence over directed networks
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作者 SHA Xingyu ZHANG Jiaqi YOU Keyou 《中山大学学报(自然科学版)(中英文)》 CAS CSCD 北大核心 2023年第5期1-23,共23页
We study distributed optimization problems over a directed network,where nodes aim to minimize the sum of local objective functions via directed communications with neighbors.Many algorithms are designed to solve it f... We study distributed optimization problems over a directed network,where nodes aim to minimize the sum of local objective functions via directed communications with neighbors.Many algorithms are designed to solve it for synchronized or randomly activated implementation,which may create deadlocks in practice.In sharp contrast,we propose a fully asynchronous push-pull gradient(APPG) algorithm,where each node updates without waiting for any other node by using possibly delayed information from neighbors.Then,we construct two novel augmented networks to analyze asynchrony and delays,and quantify its convergence rate from the worst-case point of view.Particularly,all nodes of APPG converge to the same optimal solution at a linear rate of O(λ^(k)) if local functions have Lipschitz-continuous gradients and their sum satisfies the Polyak-?ojasiewicz condition(convexity is not required),where λ ∈(0,1) is explicitly given and the virtual counter k increases by one when any node updates.Finally,the advantage of APPG over the synchronous counterpart and its linear speedup efficiency are numerically validated via a logistic regression problem. 展开更多
关键词 fully asynchronous distributed optimization linear convergence Polyak-Łojasiewicz condition
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A Cooperative Security Monitoring Mechanism Aided by Optimal Multiple Slave Cluster Heads for UASNs
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作者 Yougan Chen Wei Wang +3 位作者 Xiang Sun Yi Tao Zhenwen Liu Xiaomei Xu 《China Communications》 SCIE CSCD 2023年第5期148-169,共22页
As each cluster head(CH)sensor node is used to aggregate,fuse,and forward data from different sensor nodes in an underwater acoustic sensor network(UASN),guaranteeing the data security in a CH is very critical.In this... As each cluster head(CH)sensor node is used to aggregate,fuse,and forward data from different sensor nodes in an underwater acoustic sensor network(UASN),guaranteeing the data security in a CH is very critical.In this paper,a cooperative security monitoring mechanism aided by multiple slave cluster heads(SCHs)is proposed to keep track of the data security of a CH.By designing a low complexity“equilateral triangle algorithm(ETA)”,the optimal SCHs(named as ETA-based multiple SCHs)are selected from the candidate SCHs so as to improve the dispersion and coverage of SCHs and achieve largescale data security monitoring.In addition,by analyzing the entire monitoring process,the close form expression of the probability of the failure attack identification for the SCHs with respect to the probability of attack launched by ordinary nodes is deduced.The simulation results show that the proposed optimal ETA-based multiple SCH cooperation scheme has lower probability of the failure attack identification than that of the existing schemes.In addition,the numerical simulation results are consistent with the theoretical analysis results,thus verifying the effectiveness of the proposed scheme. 展开更多
关键词 underwater acoustic sensor networks data security cluster head nodes optimal location distribution of multiple slave cluster head nodes
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