In this paper,we consider distributed convex optimization problems on multi-agent networks.We develop and analyze the distributed gradient method which allows each agent to compute its dynamic stepsize by utilizing th...In this paper,we consider distributed convex optimization problems on multi-agent networks.We develop and analyze the distributed gradient method which allows each agent to compute its dynamic stepsize by utilizing the time-varying estimate of the local function value at the global optimal solution.Our approach can be applied to both synchronous and asynchronous communication protocols.Specifically,we propose the distributed subgradient with uncoordinated dynamic stepsizes(DS-UD)algorithm for synchronous protocol and the AsynDGD algorithm for asynchronous protocol.Theoretical analysis shows that the proposed algorithms guarantee that all agents reach a consensus on the solution to the multi-agent optimization problem.Moreover,the proposed approach with dynamic stepsizes eliminates the requirement of diminishing stepsize in existing works.Numerical examples of distributed estimation in sensor networks are provided to illustrate the effectiveness of the proposed approach.展开更多
We present an algorithm for determining the stepsize in an explicit Runge-Kutta method that is suitable when solving moderately stiff differential equations. The algorithm has a geometric character, and is based on a ...We present an algorithm for determining the stepsize in an explicit Runge-Kutta method that is suitable when solving moderately stiff differential equations. The algorithm has a geometric character, and is based on a pair of semicircles that enclose the boundary of the stability region in the left half of the complex plane. The algorithm includes an error control device. We describe a vectorized form of the algorithm, and present a corresponding MATLAB code. Numerical examples for Runge-Kutta methods of third and fourth order demonstrate the properties and capabilities of the algorithm.展开更多
Affine projection algorithm(APA)has been used to estimate the parameters of interior permanent magnet synchronous motor(IPMSM).However,there is not a strict guideline of choosing the stepsize of this algorithm to make...Affine projection algorithm(APA)has been used to estimate the parameters of interior permanent magnet synchronous motor(IPMSM).However,there is not a strict guideline of choosing the stepsize of this algorithm to make sure that the results of parameter estimation are convergent.In order to solve such problem,self-adaptive stepsize affine projection algorithm for parameter estimation of IPMSM is proposed in this paper.Compared with traditional affine projection algorithm,this method can obtain the stepsize automatically based on the operation condition,which can ensure the convergence and celerity of the process of parameter estimation.Then,on the basis of self-adaptive stepsize affine projection algorithm,a novel parameter estimation method based on square-wave current injection is proposed.By this method,the error of estimated parameter caused by stator resistance,linkage magnetic flux and dead-time voltage can be reduced effectively.Finally,the proposed parameter estimation method is verified by experiments on a 2.2-kW IPMSM drive platform.展开更多
在大规模多输入多输出系统中,最小均方误差(minimum mean square error,MMSE)算法能达到接近最优的线性信号检测性能,但是MMSE算法需要复杂的矩阵求逆运算,这限制了该算法的应用。为了降低运算复杂度,改进MMSE算法,利用Barzilai-Borwein...在大规模多输入多输出系统中,最小均方误差(minimum mean square error,MMSE)算法能达到接近最优的线性信号检测性能,但是MMSE算法需要复杂的矩阵求逆运算,这限制了该算法的应用。为了降低运算复杂度,改进MMSE算法,利用Barzilai-Borwein(BB)迭代算法来避免矩阵求逆运算,提出了结构简单的BB迭代信号检测算法,且基于信道硬化特性进一步优化了迭代初始解以加快算法的收敛速度。理论和仿真结果表明,所提出的BB迭代算法的性能优于最近提出的Neumann级数展开算法,而其复杂度相比截短阶数i=3的Neumann级数展开算法减少了一个数量级;且该算法收敛速度较快,在给定初始值的条件下,通过简单的几次迭代,能够快速接近MMSE算法的检测性能。展开更多
针对时间反演多址系统中信道的相关性会导致多用户干扰的问题,以降低用户间干扰和算法复杂度为目标,提出基于Barzilai-Borwein的共轭梯度迭代检测算法。首先通过共轭梯度迭代两次找到最速下降方向,然后通过Barzilai-Borwein沿着共轭梯...针对时间反演多址系统中信道的相关性会导致多用户干扰的问题,以降低用户间干扰和算法复杂度为目标,提出基于Barzilai-Borwein的共轭梯度迭代检测算法。首先通过共轭梯度迭代两次找到最速下降方向,然后通过Barzilai-Borwein沿着共轭梯度搜索的方向继续迭代。仿真表明,所提算法收敛速度快于Barzilai-Borwein和共轭梯度算法,且复杂度低于共轭梯度算法和最小均方误差(minimum mean square error,MMSE)算法,保持在O(N2)。展开更多
目的:为了保持大规模MIMO系统上行链路信号高检测性能同时降低实现复杂度。方法:在信号检测中,传统最小均方误差(MMSE,minimum mean square error)算法可以获得近似最优检测性能,但需要高维矩阵求逆,复杂度很高。本文通过在最速下降法和...目的:为了保持大规模MIMO系统上行链路信号高检测性能同时降低实现复杂度。方法:在信号检测中,传统最小均方误差(MMSE,minimum mean square error)算法可以获得近似最优检测性能,但需要高维矩阵求逆,复杂度很高。本文通过在最速下降法和Barzilai-Borwein迭代算法结合基础上,采用Richardson算法初始值,在此基础上对步长合理选取,提出一种改进Barzilai-Borwein信号检测方法。结果:该算法相对于MMSE复杂度降低一个数量级,并且算法误码率性能又可以与MMSE算法相当,同时该算法相对于Barzilai-Borwein和CBB(Cauchy Barzilai-Borwein)检测,复杂度提升很少,但性能有较大提升。结论:基于改进修正Barzilai-Borwein迭代大规模MIMO信号检测算法只需4次迭代就可接近MMSE,在保持高检测性能的同时实现了复杂度降低。展开更多
基金supported by the Key Research and Development Project in Guangdong Province(2020B0101050001)the National Science Foundation of China(61973214,61590924,61963030)the Natural Science Foundation of Shanghai(19ZR1476200)。
文摘In this paper,we consider distributed convex optimization problems on multi-agent networks.We develop and analyze the distributed gradient method which allows each agent to compute its dynamic stepsize by utilizing the time-varying estimate of the local function value at the global optimal solution.Our approach can be applied to both synchronous and asynchronous communication protocols.Specifically,we propose the distributed subgradient with uncoordinated dynamic stepsizes(DS-UD)algorithm for synchronous protocol and the AsynDGD algorithm for asynchronous protocol.Theoretical analysis shows that the proposed algorithms guarantee that all agents reach a consensus on the solution to the multi-agent optimization problem.Moreover,the proposed approach with dynamic stepsizes eliminates the requirement of diminishing stepsize in existing works.Numerical examples of distributed estimation in sensor networks are provided to illustrate the effectiveness of the proposed approach.
文摘We present an algorithm for determining the stepsize in an explicit Runge-Kutta method that is suitable when solving moderately stiff differential equations. The algorithm has a geometric character, and is based on a pair of semicircles that enclose the boundary of the stability region in the left half of the complex plane. The algorithm includes an error control device. We describe a vectorized form of the algorithm, and present a corresponding MATLAB code. Numerical examples for Runge-Kutta methods of third and fourth order demonstrate the properties and capabilities of the algorithm.
文摘Affine projection algorithm(APA)has been used to estimate the parameters of interior permanent magnet synchronous motor(IPMSM).However,there is not a strict guideline of choosing the stepsize of this algorithm to make sure that the results of parameter estimation are convergent.In order to solve such problem,self-adaptive stepsize affine projection algorithm for parameter estimation of IPMSM is proposed in this paper.Compared with traditional affine projection algorithm,this method can obtain the stepsize automatically based on the operation condition,which can ensure the convergence and celerity of the process of parameter estimation.Then,on the basis of self-adaptive stepsize affine projection algorithm,a novel parameter estimation method based on square-wave current injection is proposed.By this method,the error of estimated parameter caused by stator resistance,linkage magnetic flux and dead-time voltage can be reduced effectively.Finally,the proposed parameter estimation method is verified by experiments on a 2.2-kW IPMSM drive platform.
文摘在大规模多输入多输出系统中,最小均方误差(minimum mean square error,MMSE)算法能达到接近最优的线性信号检测性能,但是MMSE算法需要复杂的矩阵求逆运算,这限制了该算法的应用。为了降低运算复杂度,改进MMSE算法,利用Barzilai-Borwein(BB)迭代算法来避免矩阵求逆运算,提出了结构简单的BB迭代信号检测算法,且基于信道硬化特性进一步优化了迭代初始解以加快算法的收敛速度。理论和仿真结果表明,所提出的BB迭代算法的性能优于最近提出的Neumann级数展开算法,而其复杂度相比截短阶数i=3的Neumann级数展开算法减少了一个数量级;且该算法收敛速度较快,在给定初始值的条件下,通过简单的几次迭代,能够快速接近MMSE算法的检测性能。
文摘针对时间反演多址系统中信道的相关性会导致多用户干扰的问题,以降低用户间干扰和算法复杂度为目标,提出基于Barzilai-Borwein的共轭梯度迭代检测算法。首先通过共轭梯度迭代两次找到最速下降方向,然后通过Barzilai-Borwein沿着共轭梯度搜索的方向继续迭代。仿真表明,所提算法收敛速度快于Barzilai-Borwein和共轭梯度算法,且复杂度低于共轭梯度算法和最小均方误差(minimum mean square error,MMSE)算法,保持在O(N2)。
文摘目的:为了保持大规模MIMO系统上行链路信号高检测性能同时降低实现复杂度。方法:在信号检测中,传统最小均方误差(MMSE,minimum mean square error)算法可以获得近似最优检测性能,但需要高维矩阵求逆,复杂度很高。本文通过在最速下降法和Barzilai-Borwein迭代算法结合基础上,采用Richardson算法初始值,在此基础上对步长合理选取,提出一种改进Barzilai-Borwein信号检测方法。结果:该算法相对于MMSE复杂度降低一个数量级,并且算法误码率性能又可以与MMSE算法相当,同时该算法相对于Barzilai-Borwein和CBB(Cauchy Barzilai-Borwein)检测,复杂度提升很少,但性能有较大提升。结论:基于改进修正Barzilai-Borwein迭代大规模MIMO信号检测算法只需4次迭代就可接近MMSE,在保持高检测性能的同时实现了复杂度降低。
