中国数学规划学科发展概述

数学规划又称数学优化, 是运筹学的一个重要分支. 它主要研究在一定约束条件下, 如何求一个实数或者整数变量的实函数的最大值或者最小值. 它是运筹学和管理科学中最常用的一种建模工具和求解问题的方法, 在工程、经济和金融等领域有非常广泛的应用. 首先简单介绍数学规划的发展历史、应用领域及其主要研究方向; 然后简述数学规划的发展现状和在中国的发展进程; 最后, 讨论数学规划若干研究前沿问题与研究展望.     

一类组合优化函数的若干研究

本文研究了一个组合优化问题.利用组合数论的理论,给出了计算优化函数的一个新方法,并确定了4≤4m≤120时优化函数g(4m,6)的准确值,以及相心的优化向量.     

向量优化问题的E-真有效元及其标量化

本文研究实赋范空间中向量优化问题的真有效元.利用改进集,推广Benson-真有效元到EBenson-真有效元,推广Henig-真有效元到E-Henig-真有效元,并给出这两类E-真有效元之间的关系.在没有任何凸性假设下,利用一类非线性标量化函数,分别建立E-Benson-真有效元和E-Henig-真有效元的必要和充分最优性条件,给出它们的标量化刻画.本文所得结果推广了Kasimbeyli (2010)关于真有效元的相关结果.特别指出,本文所引入的E-Benson-真有效元的概念,称为Ⅱ-型E-Benson-真有效元,它改进了Zhao和Yang (2015)给出的相应的概念,因此能够保证向量优化问题的每一个E-Benson-真有效元都是E-有效元.     

向量优化中的二阶灵敏性分析

借助二阶相依导数的概念,研究了向量优化问题中扰动映射的二阶灵敏性.     

向量优化问题有效点集的稳定性

在不需要紧性假设下,利用拟C-凸函数及回收锥的性质,建立了向量优化问题有效点集的稳定性, 获得了一列目标函数和可行集均扰动情形下的向量优化问题与对应的向量优化问题有效点集的Painlevé Kuratowski内收敛性结果．所得结果推广和改进了相关文献(Attouch H, Riahi H. Stability results for Ekeland’s ε-variational principle and cone extremal solution; Huang X X. Stability in vector-valued and set-valued optimization)中的相应结果, 并给出例子说明了所得结果的正确性.     

集值向量优化的二阶最优性条件

对集值映射引入了高阶Clarke导数,给出了判别集值向量优化所有效性的二阶Kuhn-Tucker条件,并且,借助于集值映射的强(弱)伪凸性给出了一个弱有效解的充分条件.     

集值函数向量优化的鞍点条件

本文在局部凸拓扑向量空间中给出集值函数向量优化的鞍点条件。     

Benson真有效意义下向量优化的灵敏度分析

本文用关于集值映射的Contingent切导数定量地讨论了参数映射G（u）在Ben-son真有效意义下的扰动情况.记W（u）=Pmin[G（u）,S],y^∧∈W（u^∧）,则在某些条件下DW（u^∧,y^∧）（u）?Pmin[DG（u^∧,y^∧）（u）],而在另外一些条件下DW（u^∧,y^∧）（u）?Pmin[DG（u^∧,y^∧）（u）].     

向量优化问题锥有效解的逼近

本文引进了一种新的锥的扩张方式,并对新的扩张锥和原来的锥的关系作了讨论,借助这种新的概念,在无限维空间中给出了向量优化问题的锥有效解的近似方法．     

向量优化问题的几个收敛性结果

在文本中，获得了集合的弱有效元与真有效元的几个收敛性结果。然后，讨论了集值映射向量优化问题（VP）和它的近似问题（VP）n，在较强的假设条件下，获得了（VP）n的真有效解的几个收敛性结果。     

Vector optimization and variational-like inequalities

In this paper, some properties of pseudoinvex functions are obtained. We study the equivalence between different solutions of the vector variational-like inequality problem. Some relations between vector variational-like inequalities and vector optimization problems for non-differentiable functions under generalized monotonicity are established. J. Zafarani was partially supported by the Center of Excellence for Mathematics (University of Isfahan).     

Copositive optimization - Recent developments and applications

Due to its versatility, copositive optimization receives increasing interest in the Operational Research community, and is a rapidly expanding and fertile field of research. It is a special case of conic optimization, which consists of minimizing a linear function over a cone subject to linear constraints. The diversity of copositive formulations in different domains of optimization is impressive, since problem classes both in the continuous and discrete world, as well as both deterministic and stochastic models are covered. Copositivity appears in local and global optimality conditions for quadratic optimization, but can also yield tighter bounds for NP-hard combinatorial optimization problems. Here some of the recent success stories are told, along with principles, algorithms and applications.     

Vector optimization and generalized Lagrangian duality

In this paper, foundations of a new approach for solving vector optimization problems are introduced. Generalized Lagrangian duality, related for the first time with vector optimization, provides new scalarization techniques and allows for the generation of efficient solutions for problems which are not required to satisfy any convexity assumptions.     

Vector variational-like inequalities and non-smooth vector optimization problems

In this paper, we establish relationships between vector variational-like inequality problems and non-smooth vector optimization problems under non-smooth invexity. We identify the vector critical points, the weakly efficient points and the solutions of the non-smooth weak vector variational-like inequality problems, under non-smooth pseudo-invexity assumptions. These conditions are more general than those existing in the literature.     

Dirichlet process and its developments: a survey

The core of the nonparametric/semiparametric Bayesian analysis is to relax the particular parametric assumptions on the distributions of interest to be unknown and random, and assign them a prior. Selecting a suitable prior therefore is especially critical in the nonparametric Bayesian fitting. As the distribution of distribution, Dirichlet process (DP) is the most appreciated nonparametric prior due to its nice theoretical proprieties, modeling flexibility and computational feasibility. In this paper, we review and summarize some developments of DP during the past decades. Our focus is mainly concentrated upon its theoretical properties, various extensions, statistical modeling and applications to the latent variable models.     

鲁棒投资组合选择优化问题的研究进展

对近年来投资组合研究优化研究的热点问题——鲁棒投资组合优化研究的现状和发展趋势作了综述性研究．在投资组合选择优化的均值-方差模型的基础上,回顾了鲁棒投资组合选择优化问题的发展历史;详细地介绍了鲁棒投资组合选择优化的研究热点及国内外研究现状,就鲁棒投资组合选择优化问题的未来发展方向和主要研究内容,提出了新的观点,以期为相关领域的研究工作提供参考依据．     

Vector and matrix apportionment problems and separable convex integer optimization

The problems of (bi-)proportional rounding of a nonnegative vector or matrix, resp., are written as particular separable convex integer minimization problems. Allowing any convex (separable) objective function we use the notions of vector and matrix apportionment problems. As a broader class of problems we consider separable convex integer minimization under linear equality restrictions Ax = b with any totally unimodular coefficient matrix A. By the total unimodularity Fenchel duality applies, despite the integer restrictions of the variables. The biproportional algorithm of Balinski and Demange (Math Program 45:193–210, 1989) is generalized and derives from the dual optimization problem. Also, a primal augmentation algorithm is stated. Finally, for the smaller class of matrix apportionment problems we discuss the alternating scaling algorithm, which is a discrete variant of the well-known Iterative Proportional Fitting procedure.     

封闭渔场收入优化的捕捞规划模型

运用定量的手段,对封闭渔场可获最大净收入现值的捕捞规划问题进行了分析,建立了制定捕捞规划的理论模型,并给出了模型中参数的具体计算方法。     

Convexity of the optimal multifunctions and its consequences in vector optimization

Studies on cone-convexity of optimal multifunctions in vector optimization are given. Under the convexity assumptions we present conditions guaranteeing a continuous behaviour of the optimal multifunctions.