A branch-and-cut approach to portfolio selection with marginal risk control in a linear conic programming framework

Marginal risk represents the risk contribution of an individual asset to the risk of the entire portfolio In this paper, we investigate the portfolio selection problem with direct marginal risk control in a linear conic programming framework. ＇The optimization model involved is a nonconvex quadratically constrained quadratic programming （QCQP） problem. We first transform the QCQP problem into a linear conic programming problem, and then approximate the problem by semidefinite programming （SDP） relaxation problems over some subrectangles. In order to improve the lower bounds obtained from the SDP relaxation problems, linear and quadratic polar cuts are introduced for designing a branch-and-cut algorithm, that may yield an e -optimal global solution （with respect to feasibility and optimality） in a finite number of iterations. By exploring the special structure of the SDP relaxation problems, an adaptive branch-and-cut rule is employed to speed up the computation. The proposed algorithm is tested and compared with a known method in the literature for portfolio selection problems with hundreds of assets and tens of marginal risk control constraints.     

模型不确定性条件下稳健投资行为与资产定价

不确定性在很早以前就引起了经济学界的重视.本文通过一个异质偏好下的世代交叠模型,研究了模型不确定性偏好对资产定价的影响.结果发现,均衡时的资产价格与模型不确定性偏好成反比,并且随市场上稳健投资者数量的增加而降低.另外,本文的结论能够解释股票溢价之谜.     

基于两阶段优化的多属性决策法及其解的理论证明

针对只有部分权重信息(区间数),属性值为定值的多属性决策问题。先从局部考虑,建立一个目标规划模型,通过求解这个模型获得各方案的理想属性权重;再从全局考虑,建立一个二次规划模型,并对二次规划的最优解的存在性进行了理论证明。且给出了综合属性权重的求解公式,从而得到各方案的综合属性值,并以此对方案进行排序或择优。通过实例说明模型及方法的可行性和有效性。     

借贷利率限制下的效用投资组合

应用动态规划得到不同借贷利率情形下动态资产分配问题的HJB方程,并对指数效用、幂效用以及对数效用函数下的最优投资策略进行研究.通过求解相应的HJB方程和定义借入曲线得出最优投资组合的解析表达式,并对不同效用函数下投资者的借贷情况进行了说明.最后,给出算例对所得结论进行分析.     

基于AHP和NEULONET的供应商选择问题

供应商选择已经成为企业降低成本、提高服务与核心竞争能力的重要因素之一。利用层次分析法(AHP)在权重确定、定性问题量化以及神经逻辑网络(NEULONET)在逻辑推理上的优势来进行供应商的选择,建立了供应商选择的网络模型。该模型通过对不同的领域专家、不同的领域指标赋予不同的权重,从而改进了单纯采用AHP法存在的问题,并且利用AHP法来确定NEULONET一些层次的权重,使得网络的构造更加科学和适用。通过算例的检验,本方法是可行和有效的。     

示例学习与特征选择的规划模型方法

以扩张矩阵理论为基础,应用数学规划理论提出了一种规划模型求解方法,可以更好地实现概念学习和特征提取。与传统的启发式算法相比,采用遗传算法求解的规划模型可以找到多个全局最优解以及可行解。实例计算表明了该方法的有效性。     

Novel algorithm for distributed replicas management based on dynamic programming

1 .INTRODUCTIONCreating replica in different nodes is a popularmethod to make sure the data reliability in distrib-uted system. On one hand, multi-replica can avoidthe single-point-of-default problem; on the otherhand ,replicas in different nodes can reduce the ac-cessing delay and prevent the hot spots problem[1](when a single fileis accessedfrequently ,the nodewill become the bottleneck of the whole system) .However , with replicas increasing, the ex-pense for maintaining replicas is bec…     

动态联盟中多方案伙伴挑选问题的软计算算法

描述了虚拟企业动态联盟中，含有多个子项目的活动网络伙伴问题，以满足伙伴方案满意度下的最小化竞标花费与拖期惩罚之和为目标，并为之建立0－1整数规划模型，同时进行了方案有效性过滤，减小了解空间，量化了模糊决策，设计了嵌入模糊决策规则的遗传算法（软计算方法），使得算法具有比普通遗传算法更好好的寻优能力，在大规模问题求解上，该算法具有比分枝定界法更快的速度，结果表明了模型和算法的有效性和应用潜力。     

基于多信号流图的测试性验证试验样本选取方法

为设计优化的测试性验证试验方案,提出了故障样本优选算法.本算法介绍了多信号流图模型的形式化定义和建模步骤,利用其描述故障与测试间因果依赖关系,建立了故障-测试相关矩阵和故障-故障相关矩阵.在考虑充分覆盖的约束条件下,以试验费用小为优化目标,提出了最小故障样本集和每个故障样本数的选取方法.案例验证结果表明,该方法分析设计的试验方案节省了试验费用.     

Novel sensor selection strategy for LPI based on an improved IMMPF tracking method

Sensor platforms with active sensing equipment such as radar may betray their existence, by emitting energy that will be intercepted by enemy surveillance sensors. The radar with less emission has more excellent performance of the low probability of intercept（LPI）. In order to reduce the emission times of the radar, a novel sensor selection strategy based on an improved interacting multiple model particle filter（IMMPF） tracking method is presented. Firstly the IMMPF tracking method is improved by increasing the weight of the particle which is close to the system state and updating the model probability of every particle. Then a sensor selection approach for LPI takes use of both the target＇s maneuverability and the state＇s uncertainty to decide the radar＇s radiation time. The radar will work only when the target＇s maneuverability and the state＇s uncertainty exceed the control capability of the passive sensors. Tracking accuracy and LPI performance are demonstrated in the Monte Carlo simulations.     

Algorithm for multi-constrained path selection based on experimental analysis

1. INTRODUCTION It is a challenging problem to provide quality-of-servi- ce (QoS) guarantees in next generation high-speed network, and the QoS routing is one of the key issues of the problem. The problem of routing optimization for different QoS requirements has attracted more and more attention and has becomes a hot issue in this field[1~6]. The QoSR problem deals with the metric parameters including bandwidth, delay, delay jitter, loss probability, reliability and hop count. Based o…     

Test selection and optimization for PHM based on failure evolution mechanism model

The test selection and optimization （TSO） can improve the abilities of fault diagnosis, prognosis and health-state evalua- tion for prognostics and health management （PHM） systems. Traditionally, TSO mainly focuses on fault detection and isolation, but they cannot provide an effective guide for the design for testability （DFT） to improve the PHM performance level. To solve the problem, a model of TSO for PHM systems is proposed. Firstly, through integrating the characteristics of fault severity and propa- gation time, and analyzing the test timing and sensitivity, a testability model based on failure evolution mechanism model （FEMM） for PHM systems is built up. This model describes the fault evolution- test dependency using the fault-symptom parameter matrix and symptom parameter-test matrix. Secondly, a novel method of in- herent testability analysis for PHM systems is developed based on the above information. Having completed the analysis, a TSO model, whose objective is to maximize fault trackability and mini- mize the test cost, is proposed through inherent testability analysis results, and an adaptive simulated annealing genetic algorithm （ASAGA） is introduced to solve the TSO problem. Finally, a case of a centrifugal pump system is used to verify the feasibility and effectiveness of the proposed models and methods. The results show that the proposed technology is important for PHM systems to select and optimize the test set in order to improve their performance level.     

基于知识的规划模型构造系统研究

给出了基于知识的模型构造系统的定义，并以此概念为指导思想实现了基于知识的规划模型构造系统。详细论述了系统知识的表示与组织，并结合企业生产计划问题，描述了系统构造模型过程。     

基于选择性注意机理的高空视界覆盖节点选取

基于复杂地形约束的三维高空视界覆盖是空间分析领域中的NP难问题,其求解计算量较大。针对这个问题,提出一种基于注意认知理论的高空视界覆盖节点选取方法。其基本思想是利用认知心理学的选择性注意原理,来模拟人脑处理覆盖问题的机制,即不断有意识的选择性注意有价值的感知节点并进行注意后过滤处理。基于该机制的算法能有效降低高空视界覆盖的计算量。实验表明,提出的高空视界覆盖节点选取算法相对基于粒子群的寻优方法耗时短,且可以让用户根据实际问题要求的视界覆盖率来选择经济的非完全覆盖方案,以节省实地布置节点的开支。     

基于NP算法的CRM中客户识别特征的选择

客户识别对于CRM的实施具有重要意义，客户特征选择是客户识别中的重要问题．嵌套分割算法（NP算法）是一种新型的系统优化方法，通过对其四个算子进行确定，将其应用于具有组合优化特征的CRM中客户识别中的客户特征选择问题．并通过将NP算法应用于某人寿保险公司的客户特征选择问题，说明了该方法的有效性．该方法不但能够保证以概率1收敛于最优解，而且能够提高客户特征选择的效率．     

雷达干扰机分配的模糊多属性动态规划模型

针对雷达干扰机分配问题,提出了一种解决该问题的模糊多属性动态规划模型.设计了干扰效果评估指标及其权重,确定了各指标的隶属度函数.在探讨多台干扰机对一台雷达进行干扰的背景下,构建了单阶段模糊多属性决策模型.在此基础上,建立多阶段模糊多属性动态规划模型,解决了多台干扰机对多台雷达实施干扰的资源分配问题.最后通过具体的应用案例,给出了详细的解算步骤,计算结果验证了该模型解决雷达干扰机分配问题的有效性和适用性.     

An Integral Model for Multiclass Spatial Price Equilibrium with transportation Competition and Congestion

1IntroductionSpatialpriceequilibrium(SPE)isaPareto'soptimalitythatdeterminestheequilibriumpricesateachofregionsandtheinterregionalcommodityflowsinaprice-responsiveenvironment.Hence,thedemandandsupplyfunctionsindifferentregionsandthetransportationdataareavailablefortheinputwhileotherparameterssuchasprices,quantitiesofconsumedcommodities,quantitiesofproducedcommoditiesandorigin-destinationflows(referredtoODmatrix)canbeobtainedfromtheoutput.TheSPEmodelscanbeusedforplanninginagriculturaleconom…     

Parameter selection of support vector machine for function approximation based on chaos optimization

The support vector machine （SVM） is a novel machine learning method, which has the ability to approximate nonlinear functions with arbitrary accuracy. Setting parameters well is very crucial for SVM learning results and generalization ability, and now there is no systematic, general method for parameter selection. In this article, the SVM parameter selection for function approximation is regarded as a compound optimization problem and a mutative scale chaos optimization algorithm is employed to search for optimal paraxneter values. The chaos optimization algorithm is an effective way for global optimal and the mutative scale chaos algorithm could improve the search efficiency and accuracy. Several simulation examples show the sensitivity of the SVM parameters and demonstrate the superiority of this proposed method for nonlinear function approximation.     

基于图论的虚拟企业制造伙伴选择优化算法

虚拟企业是21世纪企业进行生产经营和市场竞争的主要模式,确定合适的,有竞争力的合作伙伴直接关系到虚拟企业的市场反应速度及合作的绩效和成败.针对此问题,提出了基于图论的优化算法的解决方案.首先描述了虚拟企业制造任务,明确了虚拟企业在选择制造伙伴时应考虑的因素,在此基础上,建立了虚拟企业制造伙伴选择的数学模型,提出以“合同网”方法选择制造伙伴,针对评标中对投标者如何选择的问题,论文给出了两种基于图论的优化算法,并以实例说明了算法的有效性.     

An extraction algorithm for a set of elementary siphons based on mixed-integer programming

Elementary siphons are useful in the development of a deadlock prevention policy for a discrete event system modeled with Petri nets.This paper proposes an algorithm to iteratively extract a set of elementary siphons in a class of Petri nets,called system of simple sequential processes with resources (S~3PR).At each iteration,by a mixed-integer programming(MIP) method,the proposed algorithm finds a maximal unmarked siphon,classifies the places in it,extracts an elementary siphon from the classified places,and adds a new constraint in order to extract the next elementary siphon.This algorithm iteratively executes until no new unmarked siphons can be found.It finally obtains a unique set of elementary siphons and avoids a complete siphon enumeration.A theoretical analysis and examples are given to demonstrate its efficiency and practical potentials.