三对角逆M矩阵的判定

1、引言 三对角逆M矩阵是指同时为三对角矩阵和逆M矩阵的一类特殊矩阵．文用图论方法探讨三对角逆M矩阵结构,给出了三对角矩阵为逆M矩阵的充分必要条件．此条件提供了判定三对角矩阵是逆M矩阵的方法,但较复杂．文讨论了这类矩阵在Hadamard积下的封闭性．由于三对角逆M矩阵在理论和应用上都有一定价值,所以,寻求一种简单而实用的判定方法是必要的．本文通过对这类矩阵结构特点的研究找到了这样一种方法．同时,由此证明了这类矩阵在Hadamard积下的封闭性．     

半正定分块矩阵和一个线性矩阵方程及其反问题

一个实的（未必对称）ｎ×ｎ矩阵Ａ称为半正定的，如果对任意非零的ｎ维行向量ｘ，均有ｘＭｘ^t≥０．本文给出了一个分块ｎ×ｎ矩阵为半正定的充要条件．另外，我们讨论了线性矩阵方程ＡＸ＝Ｂ对解附加种种条件下的解．我们应用矩阵在相抵下的标准形给出了这一方程的相容性的充要条件．还给出这个方程的反问题在对解附加各种条件下的解．     

关于H-矩阵的实用判定的注记

本文指出《H-矩阵的实用判定》一文的主要结果中的许多条件是多余的,我们用比较简捷的方法改进了该文的结果,并给出了一些新的H-矩阵的判定方法．     

上三角Toeplitz矩阵的一个结论

本得出了上三角Toeplitz矩阵关于矩阵乘法构成一交换群的结果,并给出其逆矩阵的计算方法．     

一类特殊矩阵方程的并行预处理变形共轭梯度算法

研究了求解一类矩阵方程AXB=C,提出了一种并行预处理变形共轭梯度法．该方法给出一种迭代法的预处理模式．首先给出的预处理矩阵是严格对角占优矩阵,构造并行迭代求解预处理矩阵方程的迭代格式,进而使用变形共轭梯度法并行求解．通过数值试验,预处理变形共轭梯度法与直接使用变形共轭梯度法相比较,该算法不仅有效提高了收敛速度,而且具有很高的并行性．     

一种新的行列式不变性

本文在［１］的基础上．建立了一种新的行列式不变性．     

Vandermonde矩阵的一种应用

以Vandermonde矩阵的基本性质、矩阵的特征值与迹之间的关系为理论依据，由矩阵的（理论）特征值生成的Vandermonde矩阵．构造出一种特殊的等幂和矩阵．即幂迹矩阵，在此基础上可给出判定任意n阶实矩阵的互异特征值个数的三个充要条件．以及相应的算法和自定义matlab函数．     

一类Z-矩阵的研究

称Ａ是有性质Ｐ的Ｚ－矩阵．如果Ａ是Ｚ－矩体且Ａ的每个真主子式是正的．该类矩阵包含了几个著名的矩阵类．本文主要刻画了有性质Ｐ的Ｚ－矩阵，改进了［１，４］的结果，并给出了若干著名矩阵类的一些公共特性．     

反对称矩阵的一种特殊分解

本文给出反对称矩阵的一种特殊分解．可用于最优控制等。     

不可约M－矩阵的刻画（II）

该文给出了（非）奇异Ｚ－矩阵是（非）奇异不可约Ｍ－矩阵的一些充分必要条件．     

On a Problem of M. Kambites Regarding Abundant Semigroups

A semigroup is regular if it contains at least one idempotent in each ?-class and in each ?-class. A regular semigroup is inverse if it satisfies either of the following equivalent conditions: (i) there is a unique idempotent in each ?-class and in each ?-class, or (ii) the idempotents commute. Analogously, a semigroup is abundant if it contains at least one idempotent in each ?*-class and in each ?*-class. An abundant semigroup is adequate if its idempotents commute. In adequate semigroups, there is a unique idempotent in each ?* and ?*-class. M. Kambites raised the question of the converse: in a finite abundant semigroup such that there is a unique idempotent in each ?* and ?*-class, must the idempotents commute? In this note, we provide a negative answer to this question.     

The Egalitarian Solution for Multichoice Games

In a multichoice game a coalition is characterized by the level at which each player is acting, and to each coalition a real number is assigned. A multichoice solution assigns, for each multichoice game, a numerical value to each possible activity level of each player, intended to measure the contribution of each such level to reaching the grand coalition in which each player is active at the maximal level. The paper focuses on the egalitarian multichoice solution, characterized by the properties of Efficiency, Zero Contribution, Additivity, Anonymity, and Level Symmetry. The egalitarian solution is also shown to satisfy the property of marginalism: it measures the effect of lowering, ceteris paribus, a certain activity level by one. The solution is compared to a multichoice solution studied in Klijn, Slikker, and Zarzuelo (1999). Finally, it is discussed how the formalism of this paper can be applied to the different framework of multi-attribute utilities.     

Optimization by ant algorithms: possible roles for an individual ant

In most ant algorithms, the role of each ant is to build a solution in a constructive way, basing each decision on the greedy force and the trails. However, different roles are possible for each individual ant, ranging from a negligible help in the decision process to a refined local search heuristic. In this paper, the importance of the role assigned to each ant is discussed. Three general ant methodologies are presented. Comparative results are analyzed for the well-known graph coloring problem.     

Optimization-based Distribution Planning for Consumer Electronic Items

This paper describes the application of network modeling and optimization for short-term distribution planning in an Indian electronica firm. The company has three production centres, each comprising several factories. Each day, it ships finished product from the three production centres to 23 distribution centres (namely, its branches and sub-branches) spread all over the country. The present problem may be stated as: given the planned production quantity at each production centre and the estimated demand at each distribution centre for each week of a given month, specify the quantities to be dispatched from each production centre to the various distribution centres in each week of the month. The problem is modelled as a multi-period, minimum-cost, network flow model for each product item. The paper also discusses the implementation and actual use of the model for monthly distribution planning at the company.     

Condensing timetables with target date divisible by each instructor’s number of class hours

Initial data required to construct a school timetable which can be represented as amatrix with a constant number of nonzero elements in each row and a constant set of elements in each column are considered. Conditions are determined under which this matrix can be transformed so that the sets of elements in each row and each column are preserved and the nonzero elements in every row are consecutive.     

An inventory-transportation system with stochastic demand

We study a logistic system in which a supplier has to deliver a set of products to a set of retailers to face a stochastic demand over a given time horizon. The transportation from the supplier to each retailer can be performed either directly, by expensive and fast vehicles, or through an intermediate depot, by less expensive but slower vehicles. At most one time period is required in the former case, while two time periods are needed in the latter case. A variable transportation cost is charged in the former case, while a fixed transportation cost per journey is charged in the latter case. An inventory cost is charged at the intermediate depot. The problem is to determine, for each time period and for each product, the quantity to send from the supplier to the depot, from the depot to each retailer and from the supplier to each retailer, in order to minimize the total expected cost. We first show that the classical benchmark policy, in which the demand of each product at each retailer is set equal to the average demand, can give a solution which is infinitely worse with respect to the optimal solution. Then, we propose two classes of policies to solve this problem. The first class, referred to as Horizon Policies, is composed of policies which require the solution of the overall problem over the time horizon. The second class, referred to as Reoptimization Policies, is composed of a myopic policy and several rolling-horizon policies in which the problem is reoptimized at each time period, once the demand of the time period is revealed. We evaluate the performance of each policy dynamically, by using Monte Carlo Simulation.     

The Invariant Measures of Markov Chains on Product Spaces and a Measurement of Dependence

For probability measures on product spaces, we define a notion of dependence among each coordinate. We study Markov chains on product spaces in which the time developement of each coordinate corresponds to the movement of a particle in an interacting particle system described by the Markov chain. If there is no interaction among these particles, the dependence of them decreases monotonically. We establish an inequality which states that if the interaction among each particle is small, then, in stationarity, the dependence among each particle is small.     

Multi-agent single machine scheduling

We consider the scheduling problems arising when several agents, each owning a set of nonpreemptive jobs, compete to perform their respective jobs on one shared processing resource. Each agent wants to minimize a certain cost function, which depends on the completion times of its jobs only. The cost functions we consider in this paper are maximum of regular functions (associated with each job), number of late jobs and total weighted completion time. The different combinations of the cost functions of each agent lead to various problems, whose computational complexity is analysed in this paper. In particular, we investigate the problem of finding schedules whose cost for each agent does not exceed a given bound for each agent.     

The Single Period Coverage Facility Location Problem: Lagrangean heuristic and column generation approaches

In this paper we introduce the Single Period Coverage Facility Location Problem. It is a multi-period discrete location problem in which each customer is serviced in exactly one period of the planning horizon. The locational decisions are made independently for each period, so that the facilities that are open need not be the same in different time periods. It is also assumed that at each period there is a minimum number of customers that can be assigned to the facilities that are open. The decisions to be made include not only the facilities to open at each time period and the time period in which each customer will be served, but also the allocation of customers to open facilities in their service period.     

Staffing decisions for heterogeneous workers with turnover

In this paper we consider a firm that employs heterogeneous workers to meet demand for its product or service. Workers differ in their skills, speed, and/or quality, and they randomly leave, or turn over. Each period the firm must decide how many workers of each type to hire or fire in order to meet randomly changing demand forecasts at minimal expense. When the number of workers of each type can by continuously varied, the operational cost is jointly convex in the number of workers of each type, hiring and firing costs are linear, and a random fraction of workers of each type leave in each period, the optimal policy has a simple hire- up-to/fire-down-to structure. However, under the more realistic assumption that the number of workers of each type is discrete, the optimal policy is much more difficult to characterize, and depends on the particular notion of discrete convexity used for the cost function. We explore several different notions of discrete convexity and their impact on structural results for the optimal policy.