图的L(3,2,1)-标号

无向图G的L(3,2,1)-标号是指从顶点集V(G)到非负整数集Z*的一个映射,满足:对i=1,2,3,只要dG(x,y)=i,则f(x)-f(y)|≥4-i.若一个L(3,2,1)-标号中的所有像元素都不超过整数k,则称之为k-L(3,2,1)-标号.图G的L(3,2,1)-标号数,记作3λ(G),是使得图G存在k-L(3,2,1)-标号的最小整数k.文中给出了路、圈、树等特殊图的L(3,2,1)-标号数,并给出了一般图的L(3,2,1)-标号数的一个上界.     

作业车间调度问题的改进混合遗传算法

作业车间调度是一类求解困难的组合优化问题,本文在考虑遗传算法早熟收敛问题和禁忌搜索法自适应优点的基础上,将遗传算法和禁忌搜索法相结合,提出了一种基于遗传和禁忌搜索的混合算法,并用实例对该算法进行了仿真研究.结果表明,该算法有很好的收敛精度,是可行的,与传统的算法相比较,有明显的优越性.     

Robust Dynamics and Control of a Partially Observed Markov Chain

In a seminal paper, Martin Clark (Communications Systems and Random Process Theory, Darlington, 1977, pp. 721–734, 1978) showed how the filtered dynamics giving the optimal estimate of a Markov chain observed in Gaussian noise can be expressed using an ordinary differential equation. These results offer substantial benefits in filtering and in control, often simplifying the analysis and an in some settings providing numerical benefits, see, for example Malcolm et al. (J. Appl. Math. Stoch. Anal., 2007, to appear). Clark’s method uses a gauge transformation and, in effect, solves the Wonham-Zakai equation using variation of constants. In this article, we consider the optimal control of a partially observed Markov chain. This problem is discussed in Elliott et al. (Hidden Markov Models Estimation and Control, Applications of Mathematics Series, vol. 29, 1995). The innovation in our results is that the robust dynamics of Clark are used to compute forward in time dynamics for a simplified adjoint process. A stochastic minimum principle is established.     

A policy iteration algorithm for fixed point problems with nonexpansive operators

The aim of this paper is to solve the fixed point problems:

Stationary Analysis of a Fluid Queue Driven by Some Countable State Space Markov Chain

Motivated by queueing systems playing a key role in the performance evaluation of telecommunication networks, we analyze in this paper the stationary behavior of a fluid queue, when the instantaneous input rate is driven by a continuous-time Markov chain with finite or infinite state space. In the case of an infinite state space and for particular classes of Markov chains with a countable state space, such as quasi birth and death processes or Markov chains of the G/M/1 type, we develop an algorithm to compute the stationary probability distribution function of the buffer level in the fluid queue. This algorithm relies on simple recurrence relations satisfied by key characteristics of an auxiliary queueing system with normalized input rates.      

The behavior of best -approximations as . A counterexample of convergence

In the space of summable sequences we give an example of a one-dimensional affine subspace C such that the best L_p-approximations of 0 from C fail to converge as p↓1. We thus give an answer to this problem of convergence in infinite measure spaces.     

Weak and strong convergence theorems for strict pseudo-contractions in Hilbert spaces

Let C be a closed convex subset of a real Hilbert space H and assume that T is a κ-strict pseudo-contraction on C with a fixed point, for some 0?κ<1. Given an initial guess x₀∈C and given also a real sequence {αn} in (0,1). The Mann's algorithm generates a sequence {xn} by the formula: xn+1=αnxn+(1−αn)Txn, n?0. It is proved that if the control sequence {αn} is chosen so that κ<αn<1 and , then {xn} converges weakly to a fixed point of T. However this convergence is in general not strong. We then modify Mann's algorithm by applying projections onto suitably constructed closed convex sets to get an algorithm which generates a strong convergent sequence. This result extends a recent result of Nakajo and Takahashi [K. Nakajo, W. Takahashi, Strong convergence theorems for nonexpansive mappings and nonexpansive semigroups, J. Math. Anal. Appl. 279 (2003) 372-379] from nonexpansive mappings to strict pseudo-contractions.     

Buckley–James-type of estimators under the classical case cohort design

We consider the estimation problem with classical case-cohort data. The case-cohort design was first proposed by Prentice (Biometrics 73:1–11, 1986). Most studies focus on the Cox regression model. In this paper, we consider the linear regression model. We propose an estimator which extends the Buckley–James estimator to the classical case-cohort design. In order to derive the BJE, there is an additional problem of finding the generalized maximum likelihood estimator (GMLE) of the underlying distribution functions. We propose a self-consistent algorithm for the GMLE. We also justify that the GMLE is consistent and asymptotically normally distributed under certain regularity conditions. We further present some simulation results on the asymptotic properties of the BJE and apply our procedure to a data set used in the literature.     

Greedy expansions in Banach spaces

We study convergence and rate of convergence of expansions of elements in a Banach space X into series with regard to a given dictionary . For convenience we assume that is symmetric: implies . The primary goal of this paper is to study representations of an element f∈X by a series

基于遗传禁忌算法的双资源约束下并行生产线调度研究

并行生产线调度问题兼有并行机器和流程车间调度问题的特点,是一类新型的调度问题。在考虑遗传算法早熟收敛特性和禁忌搜索法自适应优点的基础上,将遗传算法和禁忌搜索法结合起来,提出了基于遗传算法和禁忌搜索算法的双资源并行作业车间的调度优化问题算法,即考虑到了产品的调度受到机器、工人等资源制约的影响,对算法中种群的构造,适应度计算,遗传操作等方面进行了研究,最后给出了实例仿真和结论。