广义M-矩阵对角占优、行列式、转置及顺序主子矩阵的性质

广义M-矩阵A是可以分解为sI-B这种形式的矩阵,其中B和BT具有Perron-Froben ius性质,s>0.讨论了广义M-矩阵的主对角元大于零的这类广义M-矩阵的对角占优性、行列式、顺序主子矩阵、转置的性质和对称广义M-矩阵的等价条件.     

矩阵Hadamard积和Fan积特征值界的不等式

给出两个非负矩阵Hadamard积谱半径上界的一个新估计式和两个非奇异M矩阵的Fan积的最小特征值下界的新估计,估计式依赖矩阵的元素,易于计算。并通过具体例子加以比较,表明所得的结果在一定条件下更为精确。     

五对角逆M -矩阵的充分条件

通过矩阵分块的方法,探讨了五对角逆M-矩阵的结构,给出了五对角逆M-矩阵的充分条件,进一步证明了这类五对角矩阵在Hadamard积下的封闭性。     

Convergence analysis of directed signed networks via an M-matrix approach

This paper aims at solving convergence problems on directed signed networks with multiple nodes, where interactions among nodes are described by signed digraphs. The convergence analysis is achieved by matrix-theoretic and graph-theoretic tools, in which M-matrices play a central role. The fundamental digon sign-symmetry assumption upon signed digraphs can be removed with the proposed analysis approach. Furthermore, necessary and sufficient conditions are established for semi-positive and positive stabilities of Laplacian matrices of signed digraphs, respectively. A benefit of this result is that given strong connectivity, a directed signed network can achieve bipartite consensus (or state stability) if and only if the signed digraph associated with it is structurally balanced (or unbalanced). If the interactions between nodes are described by a signed digraph only with spanning trees, a directed signed network can achieve interval bipartite consensus (or state stability) if and only if the signed digraph contains a structurally balanced (or unbalanced) rooted subgraph. Simulations are given to illustrate the developed results by considering signed networks associated with digon sign-unsymmetric signed digraphs.     

一类n维Lotka-Volterra模型的局部渐近稳定性

给出模型(1)的正平衡点X~*(x_1~*,x_2~*,…,x_n~*)局部渐近稳定性的两种判别法:①如果(2)式的矩阵(?)满足|(?)||(?)|<0,(j=1,2,…,n),则X~*是局部渐近稳定的。②若-(?)是M方阵,即-(?)=sI-B,B≥0,s>ρ(B),且B是不可约的,则X~*是局部渐近稳定的。     

Stability conditions for a class of time-varying discrete-time systems

This paper deals with the stability conditions for a class of dynamical discrete-time systems, called ‘P-invariants’ and ‘not P-invariants’. Stability conditions are given in terms of polyhedral convex cones and are obtained by using some extensions on M-matrices.     

关于一类复方阵的LU分解

使用Ｍ－矩阵理论,对满足比较季节性四是Ｍ－矩阵的复矩阵类的ＬＵ分解问题进行了研究,给出了复矩阵ＬＵ分解的一些充分条件,推广了ＫｕｏＩ－ｗｅｎ关于Ｍ－矩阵的ＬＵ分解的主要结果。     

Bridging ETAQA and Ramaswami’s formula for the solution of M/G/1-type processes

M/G/1-type processes are often evaluated using matrix analytic methods. Specifically, Ramaswami’s recursive formula has been established as the numerically stable solution tool. Additionally, the ETAQA method, proposed previously, offers a more efficient alternative for the exact computation of a general class of metrics for M/G/1-type processes. However, the theoretical stability of ETAQA and its relation to Ramaswami’s method were not well understood. In this paper, we derive a new formulation that improves the numerical stability and computational performance of ETAQA. The resulting new method solves a homogeneous system of equations to obtain the aggregate probability of a finite set of classes of states from the state space of the underlying Markov chain. The new method, constructs this system of linear equations in a way similar to the method of Ramaswami, decoupling the computation of the probabilities of the first two initial classes of states from the computation of the aggregate probability. Because direct methods are used to solve this system, the decoupling implies an often significant speed-up over ETAQA. In addition, we show that the coefficients of the homogeneous system of equations define an M-matrix, which under certain conditions, is also diagonally dominant and thus can be factored stably. More importantly, we show that the new method is just an efficient way to implement Ramaswami’s method. We also discuss alternative normalization conditions for Ramaswami’s method. Our numerical experiments demonstrate the stability of our method for both stiff and well-behaved processes, and for both low and high system utilizations.     

关于非负矩阵Hadamard幂的Hadamard积

对于一般的非负矩阵A,B∈Mn,0α1,ρ[αA+(1-α)B]可能大于,等于,或者小于αρ(A)+(1-α)ρ(B),因此,谱半径不是非负矩阵上的凸函数.文中给出谱半径的对数在非负矩阵Hadamard幂的Hadamard积上是凸函数,且给出有关非负不可约矩阵Hadamard幂的Hadamard积的一些等价条件.