A Markov Model of Referral-Based Hiring and Workplace Segregation

I construct a Markov model of referral hiring to look at two issues: inequality between groups and workplace segregation. The model differs from most models of referral hiring in that it explicitly considers a firm structure where employment opportunities arise. The model suggests that referral hiring does not directly produce inequality between groups at the population level, even though firms have a bias toward hiring workers from the same group they already employ. The result highlights a difference between outcomes of referral hiring at the population and individual levels in that individual firm biases balance out when aggregated. However, referral hiring does produce segregation of groups across firms in a given industry, and the degree of segregation monotonically increases in the amount of referral hiring.     

关于GA-凸函数的若干Hadamard型不等式

利用GA-凸函数的定义及其Hadamard型不等式,得到与重积分有关的GA-凸函数Hadamard型不等式的推广和加细.     

数论中切比雪夫不等式的一点补充

利用Wallis不等式,得到素数计算函数的一个下界估计.     

Hamilton-Jacobi-Bellman inequality for the average control of piecewise deterministic Markov processes

ABSTRACT

The main goal of this paper is to study the infinite-horizon long run average continuous-time optimal control problem of piecewise deterministic Markov processes (PDMPs) with the control acting continuously on the jump intensity λ and on the transition measure Q of the process. We provide conditions for the existence of a solution to an integro-differential optimality inequality, the so called Hamilton-Jacobi-Bellman (HJB) equation, and for the existence of a deterministic stationary optimal policy. These results are obtained by using the so-called vanishing discount approach, under some continuity and compactness assumptions on the parameters of the problem, as well as some non-explosive conditions for the process.     

Viscosity Approximation Methods for Generalized Multi-Valued Nonexpansive Mappings with Applications

Abstract

In this article, we study viscosity approximation methods for generalized multi-valued nonexpansive mappings and we present some new results related to strong convergence, variational inequality, convex optimization, split and common split feasibility problems (SFPs). Some numerical computations are also presented to illustrate our results.     

Strong Convergence of a Projection-Type Method for Mixed Variational Inequalities in Hilbert Spaces

Abstract

In this article, a projection-type method for mixed variational inequalities is proposed in Hilbert spaces. The proposed method has the following nice features: (i) The algorithm is well defined whether the solution set of the problem is nonempty or not, under some mild assumptions; (ii) If the solution set is nonempty, then the sequence generated by the method is strongly convergent to the solution, which is closest to the initial point; (iii) The existence of the solutions to variational inequalities can be verified through the behavior of the generated sequence. The results presented in this article generalize and improve some known results.     

关于任意随机变量序列的强极限定理的一个注记

主要说明两两NQD随机变量序列的一些收敛性质是任意随机变量序列的强极限定理的推论.     

一类具非线性边界条件的高阶方程的奇摄动问题

通过引入伸展变量和非常规的渐近序列{∈}）,运用合成展开法,对一类具非线性边界条件的非线性高阶微分方程的奇摄动问题构造了形式渐近解,再运用微分不等式理论证明了原问题解的存在性及所得渐近近似式的一致有效性．