Understanding of the basic properties of the positive semi-definite tensor is a prerequisite for its extensive applications in theoretical and practical fields, especially for its square-root. Uniqueness of the square...Understanding of the basic properties of the positive semi-definite tensor is a prerequisite for its extensive applications in theoretical and practical fields, especially for its square-root. Uniqueness of the square-root of a positive semi-definite tensor is proven in this paper without resorting to the notion of eigenvalues, eigenvectors and the spectral decomposition of the second-order symmetric tensor.展开更多
In this paper, we further generalize the technique for constructing the normal （or pos- itive definite） and skew-Hermitian splitting iteration method for solving large sparse non- Hermitian positive definite system ...In this paper, we further generalize the technique for constructing the normal （or pos- itive definite） and skew-Hermitian splitting iteration method for solving large sparse non- Hermitian positive definite system of linear equations. By introducing a new splitting, we establish a class of efficient iteration methods, called positive definite and semi-definite splitting （PPS） methods, and prove that the sequence produced by the PPS method con- verges unconditionally to the unique solution of the system. Moreover, we propose two kinds of typical practical choices of the PPS method and study the upper bound of the spectral radius of the iteration matrix. In addition, we show the optimal parameters such that the spectral radius achieves the minimum under certain conditions. Finally, some numerical examples are given to demonstrate the effectiveness of the considered methods.展开更多
In this paper we introduce a primal-dual potential reduction algorithm for positive semi-definite programming. Using the symetric preserving scalings for both primal and dual interior matrices, we can construct an alg...In this paper we introduce a primal-dual potential reduction algorithm for positive semi-definite programming. Using the symetric preserving scalings for both primal and dual interior matrices, we can construct an algorithm which is very similar to the primal-dual potential reduction algorithm of Huang and Kortanek [6] for linear programming. The complexity of the algorithm is either O(nlog(X0 · S0/ε) or O(nlog(X0· S0/ε) depends on the value of ρ in the primal-dual potential function, where X0 and S0 is the initial interior matrices of the positive semi-definite programming.展开更多
采用频率测量实现目标定位具有成本低、可靠性高的特点,仅利用到达频率差(frequency difference of arrival,FDOA)测量,提出了一种静态目标位置的精确定位方法。针对所建立的频率测量方程的高度非线性这一问题,通过引入辅助变量,将其转...采用频率测量实现目标定位具有成本低、可靠性高的特点,仅利用到达频率差(frequency difference of arrival,FDOA)测量,提出了一种静态目标位置的精确定位方法。针对所建立的频率测量方程的高度非线性这一问题,通过引入辅助变量,将其转化为矩阵形式的伪线性方程;然后利用半正定松弛(semi-definite relaxation,SDR)方法将非凸的加权最小二乘(weighted least square,WLS)问题松弛为半正定规划(semidefinite programming,SDP)问题,从而进一步精确估计未知变量;最后对所提出方法的均方根误差(rootmean-square error,RMSE)进行了分析,以验证其性能。仿真结果表明,在较低的高斯噪声水平下,所采用的半正定松弛方法的性能能够达到克拉美罗下界(Cramer-Rao lower bound,CRLB),且该算法对几何形状具有较高的鲁棒性;此外,在使用较少数量的传感器时,其RMSE性能要优于两阶段加权最小二乘(two-stage weighted least square,TSWLS)法。展开更多
By presenting a counterexample, the author of paper （ZHAO Li-feng. J. Math. Res. Exposition, 2007, 27（4）： 949-954） declared that some assertions in papers of LU Yun-xia, ZHANG Shu-qing （J. Math. Res. Exposition,...By presenting a counterexample, the author of paper （ZHAO Li-feng. J. Math. Res. Exposition, 2007, 27（4）： 949-954） declared that some assertions in papers of LU Yun-xia, ZHANG Shu-qing （J. Math. Res. Exposition, 1999, 19（3）： 598-600）, HE Gan-tong （J. Math. Res. Exposition, 2002, 22（1）： 79-82） and YUAN Hui-ping （J. Math. Res. Exposition, 2001, 21（3）： 464-468） are wrong. In this note, we point out that the counterexample is wrong. Further discussion on these assertions and some related results are also given.展开更多
文摘Understanding of the basic properties of the positive semi-definite tensor is a prerequisite for its extensive applications in theoretical and practical fields, especially for its square-root. Uniqueness of the square-root of a positive semi-definite tensor is proven in this paper without resorting to the notion of eigenvalues, eigenvectors and the spectral decomposition of the second-order symmetric tensor.
文摘In this paper, we further generalize the technique for constructing the normal （or pos- itive definite） and skew-Hermitian splitting iteration method for solving large sparse non- Hermitian positive definite system of linear equations. By introducing a new splitting, we establish a class of efficient iteration methods, called positive definite and semi-definite splitting （PPS） methods, and prove that the sequence produced by the PPS method con- verges unconditionally to the unique solution of the system. Moreover, we propose two kinds of typical practical choices of the PPS method and study the upper bound of the spectral radius of the iteration matrix. In addition, we show the optimal parameters such that the spectral radius achieves the minimum under certain conditions. Finally, some numerical examples are given to demonstrate the effectiveness of the considered methods.
基金This research was partially supported by a fund from Chinese Academy of Science,and a fund from the Personal Department of the State Council.It is also sponsored by scientific research foundation for returned overseas Chinese Scholars,State Education
文摘In this paper we introduce a primal-dual potential reduction algorithm for positive semi-definite programming. Using the symetric preserving scalings for both primal and dual interior matrices, we can construct an algorithm which is very similar to the primal-dual potential reduction algorithm of Huang and Kortanek [6] for linear programming. The complexity of the algorithm is either O(nlog(X0 · S0/ε) or O(nlog(X0· S0/ε) depends on the value of ρ in the primal-dual potential function, where X0 and S0 is the initial interior matrices of the positive semi-definite programming.
文摘采用频率测量实现目标定位具有成本低、可靠性高的特点,仅利用到达频率差(frequency difference of arrival,FDOA)测量,提出了一种静态目标位置的精确定位方法。针对所建立的频率测量方程的高度非线性这一问题,通过引入辅助变量,将其转化为矩阵形式的伪线性方程;然后利用半正定松弛(semi-definite relaxation,SDR)方法将非凸的加权最小二乘(weighted least square,WLS)问题松弛为半正定规划(semidefinite programming,SDP)问题,从而进一步精确估计未知变量;最后对所提出方法的均方根误差(rootmean-square error,RMSE)进行了分析,以验证其性能。仿真结果表明,在较低的高斯噪声水平下,所采用的半正定松弛方法的性能能够达到克拉美罗下界(Cramer-Rao lower bound,CRLB),且该算法对几何形状具有较高的鲁棒性;此外,在使用较少数量的传感器时,其RMSE性能要优于两阶段加权最小二乘(two-stage weighted least square,TSWLS)法。
基金Supported by the Natural Science Foundation of Science and Technology Office of Guizhou Province (Grant No. J[2006]2002)
文摘By presenting a counterexample, the author of paper （ZHAO Li-feng. J. Math. Res. Exposition, 2007, 27（4）： 949-954） declared that some assertions in papers of LU Yun-xia, ZHANG Shu-qing （J. Math. Res. Exposition, 1999, 19（3）： 598-600）, HE Gan-tong （J. Math. Res. Exposition, 2002, 22（1）： 79-82） and YUAN Hui-ping （J. Math. Res. Exposition, 2001, 21（3）： 464-468） are wrong. In this note, we point out that the counterexample is wrong. Further discussion on these assertions and some related results are also given.
