交替投影算法求解非负逆特征值问题
Alternating Projection Method for Solving Nonnegative Inverse Eigenvalue Problems
摘要: 通过把给定部分特征对的非负逆特征值问题转化为一个凸可行性问题,提出交替投影算法求解该问题。建立了这一算法的线性收敛性。最后,通过数值例子,比较了交替投影算法和非光滑牛顿法(白等人2011年提出)的收敛效率。数值实验结果表明,交替投影算法总是能收敛到问题的解,而非光滑牛顿法在一些情形下求不出解。此外,交替投影算法收敛的效率也比非光滑牛顿法高。
Abstract: The alternating projection method is proposed for solving nonnegative inverse eigenvalue prob-lems with partial eigendata, by reformulating the problem as a convex feasibility problem. The (linear) convergnece property of the method is established. At last, some numerical experiments are provided to compare the method with the non-smooth Newton algorithm proposed by Bai et al. in 2011. Numerical experiment results show that the alternative projection algorithm always converges to the solution of the problem, while the non-smooth Newton method fails to find the solution in some cases. In addition, the alternative projection algorithm is more efficient than the non-smooth Newton method.
文章引用:杨丹, 王湘美. 交替投影算法求解非负逆特征值问题[J]. 运筹与模糊学, 2021, 11(1): 9-14. https://doi.org/10.12677/ORF.2021.111002

参考文献

[1] Chu, M.T. and Golub, G.H. (2002) Structured Inverse Eigenvalue Problems. Acta Numerica, 11, 1-71. [Google Scholar] [CrossRef
[2] Egleston, P.D, Lenker, T. and Narayan, S.K. (2004) The Nonnegative Inverse Eigenvalue Problem. Linear Algebra and its Applications, 379, 475-490. [Google Scholar] [CrossRef
[3] Chu, M.T. and Driessel, K.R. (1991) Constructing Symmetric Nonnegative Matrices with Prescribed Eigenvalues by Difffferential Equations. SIAM Journal on Mathematical Analysis, 22, 1372-1387. [Google Scholar] [CrossRef
[4] Hald, H.O. (1972) On Discrete and Numerical Invers Sturm-Liouville Problems. Ph.D. Thesis. New York University, New York.
[5] Li, N. (1997) A Matrix Inverse Ei-genvalue Problem and Its Application. Linear Algebra and its Applications, 266, 143-152. [Google Scholar] [CrossRef
[6] Deakin, A.S. and Luke, J.M. (1992) On the Inverse Eigen-value Problem for Matrices. Journal of Physics A: Mathematical and General, 25, 635-648. [Google Scholar] [CrossRef
[7] Chu, M.T. and Golub, G.H. (2005) Inverse Eigenvalue Problems: Theory, Algorithms and Applications. Oxford University Press, Oxford.
[8] Chen, X. and Liu, D.L. (2011) Isospectral Flow Method for Nonnegative Inverse Eigenvalue Problem with Prescribed Structure. Journal of Computational & Ap-plied Mathematics, 235, 3990-4002. [Google Scholar] [CrossRef
[9] Robert, O. (2006) Numerical Methodsfor Solving Inverse Eigen-value Problems for Nnonnegative Matrices. Society for Industrial and Applied, 28, 190-212. [Google Scholar] [CrossRef
[10] Bai, Z.J., Stefano, S.C. and Zhao, Z. (2012) Nonnegative Inverse Eigen-value Problems with Partial Eigendata. Numerische Mathematik, 120, 387-431. [Google Scholar] [CrossRef
[11] Chu, M.T., Diele, F. and Sgura, I. (2004) Gradient Flow Method for Matrix Completion with Prescribed Eigenvalues. Linear Algebra and its Applications, 379, 85-112. [Google Scholar] [CrossRef
[12] Loewy, R. and London, D.A. (1978) Note on an Inverse Problem for Nonnegative Matrices. Linear and Multilinear Algebra, 6, 83-90. [Google Scholar] [CrossRef
[13] Clarke, F.H. (1983) Optimization and Nonsmooth Analysis. Wiley, New York.
[14] 许以超. 线性代数与矩阵论[M]. 北京: 高等教育出版社, 2008.
[15] 朱元国, 饶玲, 严涛, 张军, 李成宝. 矩阵分析与计算[M]. 北京: 国防工业出版社, 2010.
[16] Bauschke, H.H. and Borwein, J.M. (1996) On Projection Algorithms for Solving Convex Feasibility Problems. SIAM Review, 38, 367-426. [Google Scholar] [CrossRef
[17] Zhao, X.P., Ng, K.F., Li, C. and Yao, C.H. (2018) Linear Reg-ularity and Linear Convergence of Projection-Based Methods for Solving Convex Feasibility Problems. Applied Mathe-matics & Optimization, 78, 613-641. [Google Scholar] [CrossRef