In this work we will consider asynchronous iteration algorithms. As is well known in multiprocessor computers the parallel application of iterative methods often shows poor scaling and less optimal parallel efficiency...In this work we will consider asynchronous iteration algorithms. As is well known in multiprocessor computers the parallel application of iterative methods often shows poor scaling and less optimal parallel efficiency. The ordinary iterative asynchronous method often has much better parallel efficiency as they almost never need to wait to communicate between possessors. We will study probabilistic approach in asynchronous iteration algorithms and present a mathematical description of this computational process to the multiprocessor environment. The result of our simple numerical experiments shows a convergence and efficiency of asynchronous iterative processes for considered nonlinear problems.展开更多
In this paper, the stability of iterative learning control with data dropouts is discussed. By the super vector formulation, an iterative learning control （ILC） system with data dropouts can be modeled as an asynchr...In this paper, the stability of iterative learning control with data dropouts is discussed. By the super vector formulation, an iterative learning control （ILC） system with data dropouts can be modeled as an asynchronous dynamical system with rate constraints on events in the iteration domain. The stability condition is provided in the form of linear matrix inequalities （LMIS） depending on the stability of asynchronous dynamical systems. The analysis is supported by simulations.展开更多
In this paper, the asynchronous versions of classical iterative methods for solving linear systems of equations are considered. Sufficient conditions for convergence of asynchronous relaxed processes are given for H-m...In this paper, the asynchronous versions of classical iterative methods for solving linear systems of equations are considered. Sufficient conditions for convergence of asynchronous relaxed processes are given for H-matrix by which nor only the requirements of [3] on coefficient matrix are lowered, but also a larger region of convergence than that in [3] is obtained.展开更多
In asynchronous Multiple-Input-Multiple-Output Orthogonal Frequency Division Multiplexing(MIMO-OFDM) over the selective Rayleigh fading channel,the performance of the existing linear detection algorithms improves slow...In asynchronous Multiple-Input-Multiple-Output Orthogonal Frequency Division Multiplexing(MIMO-OFDM) over the selective Rayleigh fading channel,the performance of the existing linear detection algorithms improves slowly as the Signal Noise Ratio (SNR) increases.To improve the performance of asynchronous MIMO-OFDM,a low complexity iterative detection algorithm based on linear precoding is proposed in this paper.At the transmitter,the transmitted signals are spread by precoding matrix to achieve the space-frequency diversity gain,and low complexity iterative Interference Cancellation(IC) algorithm is used at the receiver,which relieves the error propagation by the precoding matrix.The performance improvement is verified by simulations.Under the condition of 4 transmitting antennas and 4 receiving antennas at the BER of 10-4,about 6 dB gain is obtained by using our proposed algorithm compared with traditional algorithm.展开更多
为解决多基站定位模型中基站之间同步代价高的问题,提出了一种基于多根长馈线天线基站的到达时间差(Time Difference of Arrival,TDOA)定位模型,给出了模型方程和求解方法,该方法将复杂的3对距离差方程组转化为1个一元八次方程,然后采用...为解决多基站定位模型中基站之间同步代价高的问题,提出了一种基于多根长馈线天线基站的到达时间差(Time Difference of Arrival,TDOA)定位模型,给出了模型方程和求解方法,该方法将复杂的3对距离差方程组转化为1个一元八次方程,然后采用Aberth-Newton迭代法来迭代求解方程。通过计算机仿真验证了基于多根长馈线天线基站的TDOA定位模型和解法的有效性,并对该模型的多解问题进行了分析,用优化基站布局的方案,解决了定位模型的唯一解问题。本定位模型在覆盖范围数百米时,定位精度可达分米级。展开更多
For the large sparse systems of linear and nonlinear equations, a new class of generalized asynchronous parallel multisplitting iterative method is presented, and its convergence theory is established under suitable c...For the large sparse systems of linear and nonlinear equations, a new class of generalized asynchronous parallel multisplitting iterative method is presented, and its convergence theory is established under suitable conditions. This method not only unifies the discussions of various existing asynchronous multisplitting iterations, but also affords new algorithmic and theoretical results for the parallel solution of large sparse system of linear equations. Besides its generality, this method is also much more suitable for implementing on the MIMD multiprocessor systems.展开更多
In the sense of the nonlinear multisplitting and based on the principle of suffi-ciently using the delayed information, we propose models of asynchronous parallelaccelerated overrelaxation iteration methods for solvin...In the sense of the nonlinear multisplitting and based on the principle of suffi-ciently using the delayed information, we propose models of asynchronous parallelaccelerated overrelaxation iteration methods for solving large scale system of non-linear equations. Under proper conditions, we set up the local convergence theoriesof these new method models.展开更多
A class of asynchronous matrix multi-splitting multi-parameter relaxation methods, including the asynchronous matrix multisplitting SAOR, SSOR and SGS methods as well. as the known asynchronous matrix multisplitting A...A class of asynchronous matrix multi-splitting multi-parameter relaxation methods, including the asynchronous matrix multisplitting SAOR, SSOR and SGS methods as well. as the known asynchronous matrix multisplitting AOR, SOR and GS methods, etc., is proposed for solving the large sparse systems of linear equations by making use of the principle of sufficiently using the delayed information. These new methods can greatly execute the parallel computational efficiency of the MIMD-systems, and are shown to be convergent when the coefficient matrices are H-matrices. Moreover, necessary and sufficient conditions ensuring the convergence of these methods are concluded for the case that the coefficient matrices are L-matrices.展开更多
Presents a class of relaxed asynchronous parallel multisplitting iterative methods for solving the linear complementarity problem on multiprocessor systems. Establishment of the methods; Convergence theories; Numerica...Presents a class of relaxed asynchronous parallel multisplitting iterative methods for solving the linear complementarity problem on multiprocessor systems. Establishment of the methods; Convergence theories; Numerical results.展开更多
This paper proposes a class of asynchronous block iterative methods for solving large scale nonlinear equations F(x)=0 and proves local convergence. This method splits F into p blocks, then does the asynch...This paper proposes a class of asynchronous block iterative methods for solving large scale nonlinear equations F(x)=0 and proves local convergence. This method splits F into p blocks, then does the asynchronous parallel iteration on the p multiprocessor with shared memory. Because each processor need only solve equations with a low dimension and there is no synchronous waiting time, the parallel efficiency can be increased. Finally, we give the results of the numerical test of three kinds of Newton like asynchronous block iteration methods which run well on a multiprocessor system. These results show that the parallel efficiency is very high.展开更多
文摘In this work we will consider asynchronous iteration algorithms. As is well known in multiprocessor computers the parallel application of iterative methods often shows poor scaling and less optimal parallel efficiency. The ordinary iterative asynchronous method often has much better parallel efficiency as they almost never need to wait to communicate between possessors. We will study probabilistic approach in asynchronous iteration algorithms and present a mathematical description of this computational process to the multiprocessor environment. The result of our simple numerical experiments shows a convergence and efficiency of asynchronous iterative processes for considered nonlinear problems.
基金supported by General Program (No. 60774022)State Key Program (No. 60834001) of National Natural Science Foundation of China
文摘In this paper, the stability of iterative learning control with data dropouts is discussed. By the super vector formulation, an iterative learning control （ILC） system with data dropouts can be modeled as an asynchronous dynamical system with rate constraints on events in the iteration domain. The stability condition is provided in the form of linear matrix inequalities （LMIS） depending on the stability of asynchronous dynamical systems. The analysis is supported by simulations.
文摘In this paper, the asynchronous versions of classical iterative methods for solving linear systems of equations are considered. Sufficient conditions for convergence of asynchronous relaxed processes are given for H-matrix by which nor only the requirements of [3] on coefficient matrix are lowered, but also a larger region of convergence than that in [3] is obtained.
基金supported by the Hi-Tech Research and Development Program of China under Grant No.2009AA01Z236the National Natural Science Foundation of China under Grants No.60902027,No.60832007 and No.60901018+1 种基金the Funds under Grant No.9140A21030209DZ02the Fundamental Research Funds for the Central Universities under Grants No.ZYGX2009J008,No.ZYGX2009J010
文摘In asynchronous Multiple-Input-Multiple-Output Orthogonal Frequency Division Multiplexing(MIMO-OFDM) over the selective Rayleigh fading channel,the performance of the existing linear detection algorithms improves slowly as the Signal Noise Ratio (SNR) increases.To improve the performance of asynchronous MIMO-OFDM,a low complexity iterative detection algorithm based on linear precoding is proposed in this paper.At the transmitter,the transmitted signals are spread by precoding matrix to achieve the space-frequency diversity gain,and low complexity iterative Interference Cancellation(IC) algorithm is used at the receiver,which relieves the error propagation by the precoding matrix.The performance improvement is verified by simulations.Under the condition of 4 transmitting antennas and 4 receiving antennas at the BER of 10-4,about 6 dB gain is obtained by using our proposed algorithm compared with traditional algorithm.
文摘为解决多基站定位模型中基站之间同步代价高的问题,提出了一种基于多根长馈线天线基站的到达时间差(Time Difference of Arrival,TDOA)定位模型,给出了模型方程和求解方法,该方法将复杂的3对距离差方程组转化为1个一元八次方程,然后采用Aberth-Newton迭代法来迭代求解方程。通过计算机仿真验证了基于多根长馈线天线基站的TDOA定位模型和解法的有效性,并对该模型的多解问题进行了分析,用优化基站布局的方案,解决了定位模型的唯一解问题。本定位模型在覆盖范围数百米时,定位精度可达分米级。
文摘For the large sparse systems of linear and nonlinear equations, a new class of generalized asynchronous parallel multisplitting iterative method is presented, and its convergence theory is established under suitable conditions. This method not only unifies the discussions of various existing asynchronous multisplitting iterations, but also affords new algorithmic and theoretical results for the parallel solution of large sparse system of linear equations. Besides its generality, this method is also much more suitable for implementing on the MIMD multiprocessor systems.
文摘In the sense of the nonlinear multisplitting and based on the principle of suffi-ciently using the delayed information, we propose models of asynchronous parallelaccelerated overrelaxation iteration methods for solving large scale system of non-linear equations. Under proper conditions, we set up the local convergence theoriesof these new method models.
基金Project 19601036 supported by the National Natural Science Foundation of China.
文摘A class of asynchronous matrix multi-splitting multi-parameter relaxation methods, including the asynchronous matrix multisplitting SAOR, SSOR and SGS methods as well. as the known asynchronous matrix multisplitting AOR, SOR and GS methods, etc., is proposed for solving the large sparse systems of linear equations by making use of the principle of sufficiently using the delayed information. These new methods can greatly execute the parallel computational efficiency of the MIMD-systems, and are shown to be convergent when the coefficient matrices are H-matrices. Moreover, necessary and sufficient conditions ensuring the convergence of these methods are concluded for the case that the coefficient matrices are L-matrices.
基金The Special Funds For Major State Basic Research Project G1999032803.
文摘Presents a class of relaxed asynchronous parallel multisplitting iterative methods for solving the linear complementarity problem on multiprocessor systems. Establishment of the methods; Convergence theories; Numerical results.
基金Supported by the National Natural Scie-nce Foundation of China
文摘This paper proposes a class of asynchronous block iterative methods for solving large scale nonlinear equations F(x)=0 and proves local convergence. This method splits F into p blocks, then does the asynchronous parallel iteration on the p multiprocessor with shared memory. Because each processor need only solve equations with a low dimension and there is no synchronous waiting time, the parallel efficiency can be increased. Finally, we give the results of the numerical test of three kinds of Newton like asynchronous block iteration methods which run well on a multiprocessor system. These results show that the parallel efficiency is very high.
