浅析几类双输入排队系统模型

从矩阵几何解的角度出发,分析了七种类型的Geom/Geom/(Geom/Geom)/H 双输入排队系统.对这几个模型进行了描述,使用拟生灭过程探讨了各类模型的矩阵结构,并给出了其状态转移概率矩阵.     

基于严格α-双链对角占优矩阵的非奇H-矩阵的判定准则

研究了非奇H-矩阵的判定问题.先给出了几个判定严格α-双链对角占优矩阵的充要条件,进一步利用矩阵对角占优理论得到了判定非奇H-矩阵的一些充分条件,推广和改进了已有的相关结果,并用数值算例说明了这些判定方法的有效性.     

Local reconstruction of low‐rank matrices and subspaces

We study the problem of reconstructing a low‐rank matrix, where the input is an n × m matrix M over a field and the goal is to reconstruct a (near‐optimal) matrix that is low‐rank and close to M under some distance function Δ. Furthermore, the reconstruction must be local, i.e., provides access to any desired entry of by reading only a few entries of the input M (ideally, independent of the matrix dimensions n and m). Our formulation of this problem is inspired by the local reconstruction framework of Saks and Seshadhri (SICOMP, 2010). Our main result is a local reconstruction algorithm for the case where Δ is the normalized Hamming distance (between matrices). Given M that is ‐close to a matrix of rank (together with d and ), this algorithm computes with high probability a rank‐d matrix that is ‐close to M. This is a local algorithm that proceeds in two phases. The preprocessing phase reads only random entries of M, and stores a small data structure. The query phase deterministically outputs a desired entry by reading only the data structure and 2d additional entries of M. We also consider local reconstruction in an easier setting, where the algorithm can read an entire matrix column in a single operation. When Δ is the normalized Hamming distance between vectors, we derive an algorithm that runs in polynomial time by applying our main result for matrix reconstruction. For comparison, when Δ is the truncated Euclidean distance and , we analyze sampling algorithms by using statistical learning tools. A preliminary version of this paper appears appears in ECCC, see: http://eccc.hpi-web.de/report/2015/128/ © 2017 Wiley Periodicals, Inc. Random Struct. Alg., 51, 607–630, 2017     

矩阵优化扰动性分析的若干进展

由于近年来实际问题特别是大数据应用的发展,矩阵优化问题越来越得到优化研究者,甚至是其他领域的研究者的高度关注,成为热点问题.优化问题的扰动性分析是优化理论研究的基础与核心,为包括算法设计在内的优化研究提供重要的理论基础.由于矩阵优化问题的非多面体性,使得相应扰动分析理论的研究本质上与经典的多面体优化问题(非线性规划)不同.结合文献[1,2],简要介绍矩阵优化扰动性分析方面取得的若干最新进展.     

高维约束矩阵回归问题

高维约束矩阵回归是指高维情况下带非凸约束的多响应多预测统计回归问题,其数学模型是一个NP-难的矩阵优化,它在机器学习与人工智能、医学影像疾病诊疗、基因表达分析、脑神经网络、风险管理等领域有广泛应用.从高维约束矩阵回归的优化理论和算法两方面总结和评述这些新成果,同时,列出了相应的重要文献.     

On a difference equation of the second order with an exponential coefficient

This paper is devoted to a generalization of some previous results, as we completely solve a linear homogeneous difference equation of the second order with an exponential coefficient.     

Non-synchronous oscillations in four-dimensional nonlinear semelparous Leslie matrix models

In a certain class of semelparous Leslie matrix models, either a positive equilibrium is stable and an invariant set on the boundary of the nonnegative cone is unstable or vice versa generically if the model dimension is two or three. This dynamic dichotomy is expected to be failed in the four-dimensional case. Our study focuses on a semelparous Leslie matrix model with specific nonlinearities and rigorously proves that the dynamic dichotomy does not hold in the four-dimensional case. This result is derived by showing that the four-dimensional semelparous Leslie matrix model can be uniformly persistent with respect to the boundary of the nonnegative cone even if there exists an unstable positive equilibrium. In such a situation, there are no missing age-classes but population oscillation occurs.     

经方量化归经研究中的向量矩阵应用

在经方量化归经研究中发现,药物组成的归经量化问题,若以五维向量表示更能体现其药物的归经方向和归经比例.利用向量矩阵分析方法给出了方剂归经量化的一种全新的量化模型体系,并以此研究方剂归经的量化规律及其应用特点.     

An analysis on the efficiency of Euler's method for computing the matrix pth root

It is shown that the matrix sequence generated by Euler's method starting from the identity matrix converges to the principal pth root of a square matrix, if all the eigenvalues of the matrix are in a region including the one for Newton's method given by Guo in 2010. The convergence is cubic if the matrix is invertible. A modification version of Euler's method using the Schur decomposition is developed. Numerical experiments show that the modified algorithm has the overall good numerical behavior.