Approximation Algorithms for the Multiple Knapsack Problem with Assignment Restrictions

Motivated by a real world application, we study the multiple knapsack problem with assignment restrictions (MKAR). We are given a set of items, each with a positive real weight, and a set of knapsacks, each with a positive real capacity. In addition, for each item a set of knapsacks that can hold that item is specified. In a feasible assignment of items to knapsacks, each item is assigned to at most one knapsack, assignment restrictions are satisfied, and knapsack capacities are not exceeded. We consider the objectives of maximizing assigned weight and minimizing utilized capacity.We focus on obtaining approximate solutions in polynomial computational time. We show that simple greedy approaches yield 1/3-approximation algorithms for the objective of maximizing assigned weight. We give two different 1/2-approximation algorithms: the first one solves single knapsack problems successively and the second one is based on rounding the LP relaxation solution. For the bicriteria problem of minimizing utilized capacity subject to a minimum requirement on assigned weight, we give an (1/3,2)-approximation algorithm.     

Bayesian prediction of waiting times in stochastic models

The authors show how saddlepoint techniques lead to highly accurate approximations for Bayesian predictive densities and cumulative distribution functions in stochastic model settings where the prior is tractable, but not necessarily the likelihood or the predictand distribution. They consider more specifically models involving predictions associated with waiting times for semi‐Markov processes whose distributions are indexed by an unknown parameter θ. Bayesian prediction for such processes when they are not stationary is also addressed and the inverse‐Gaussian based saddlepoint approximation of Wood, Booth & Butler (1993) is shown to accurately deal with the nonstationarity whereas the normal‐based Lugannani & Rice (1980) approximation cannot, Their methods are illustrated by predicting various waiting times associated with M/M/q and M/G/1 queues. They also discuss modifications to the matrix renewal theory needed for computing the moment generating functions that are used in the saddlepoint methods.     

一种基于加权F-范数的半正定矩阵的逼近方法

正定性是许多金融预测模型的重要假设前提，然而从实际样本中得到的相关系数矩阵并不能保证其正定性。为此在介绍如何根据样本设定相关系数矩阵以及范数逼近原理的基础上，如何根据该原理找到与之最接近的相关系数矩阵，即最接近的单位对角半正定对称矩阵。通过实证，验证了其方法的有效性。     

供应商管理库存环境下的分销网络连续近似模型

本文在现有连续近似(CA)模型的基础上,从供应商管理库存(VMI)角度出发,引入了PowerofTwo(POT)周期配送策略,构建了VMI环境下分销网络设计的CA模型。该模型的目标函数不仅包括了分销中心的建设 运营费用、运输费用,而且引进了实际中关注的存储费和订货费;该模型在确定分销网络结构的同时,也确定了各分销中心的库存策略和对其客户的配送策略。文中给出了模型的解法,利用此解法对算例进行求解,并分析了求解的结果,得出各参数变化对解的影响。     

三台平行机排序的一个复合半在线问题的算法

本文讨论一个三台平行机半在线排序问题.对预先知道工件的总加工时间和最大的工件的加工时间的复合半在线模型,我们证明了不存在半在线算法,其竞争比为4/3,并给出了一个竞争比为7/5的半在线算法,两者的差距小于0.067.     

A note on the most powerful invariant test of Rayleigh against exponential distribution

Abstract

The problem of testing Rayleigh distribution against exponentiality, based on a random sample of observations is considered. This problem arises in survival analysis, when testing a linearly increasing hazard function against a constant hazard function. It is shown that for this problem the most powerful invariant test is equivalent to the “ratio of maximized likelihoods” (RML) test. However, since the two families are separate, the RML test statistic does not have the usual asymptotic chi-square distribution. Normal and saddlepoint approximations to the distribution of the RML test statistic are derived. Simulations show that saddlepoint approximation is more accurate than the normal approximation, especially for tail probabilities that are the main values of interest in hypothesis testing.     

M-estimation and model identification based on double SCAD penalization

M-estimation is a widely used method for robust statistical inference. In this article, using a B-spline series approximation with a double smoothly clipped absolute deviation penalization, we solve the problem of simultaneous variable selection and parametric component identification in a non parametric additive model. The theoretical properties of the double non concave penalized M-estimation are established. The proposed approach is resistant to heavy-tailed errors or outliers in the responses. Simulation studies for finite-sample cases are conducted and a real dataset is also analyzed for illustration of this new approach.     

Graphical methods for ranking data

Graphical methods are presented for the analysis of ranking data collected from g groups of rankers. The data provided by a single individual consist of the ranks of r objects. The sample space is the space of all permutations and has cardinality r! In order to reduce the dimensionality of the data and to study the interrelationships among rankers and items, a two-stage approach is proposed. First, transformations motivated by various metrics on permutations are defined. In particular, the Kendall metric gives rise to pairwise comparisons. Then, the transformed data are analyzed using results in connection with the generalized singular-value decomposition of a matrix. The methods are illustrated on two examples.     

一类多元Bernstein算子的加权逼近

主要讨论了一类二元的Bernstein算子的加权逼近问题。首先，我们举例说明在通常的加权范数下它是无界的。然后我们给出了一类新的加权范数，在此范数下它是有界的。最后我们利用一类新的加权K一泛函给出了二元Bernstein算子加Jacobi权逼近的特征刻划。