一般形式多阶段有补偿问题的广义对偶理论

本文在文［１］的基础上，讨论一般形式多阶段有补偿非线性随机规划问题的广义对偶理论与最优化性条件．通过发掘凸规划对偶理论的本质，首先推广了与通常规划问题对偶理论有关的概念的含义，由此构造出所论问题在等价意义下的广义原始泛函与广义对偶泛函，进而得到其广义对偶理论，所得结论不仅能恰当合理地反映问题本身的属性，而且有关定理的表述形式简明、结论较强，可直接应用于多阶段有补偿问题的其它理论研究与数值求解算法的设计中去．上述结果与所用研究方法均推广和发展了通常的对偶理论     

一类单边约束问题的数值方法

本文分别基于原始变分形式与对偶混合变分形式，对一类单边约束问题进行了数值求解，提出了求解离散对偶混合变分问题的Uzawa型算法，并用数值例子验证了算法的有效性．     

求解不等式约束优化问题的一个改进算法

本文改进了一个求解不等式约束优化问题的对偶算法，建立了一个相应的算法，进一步证明了该算法的收敛性．最后，给出数值结果以验证该算法的有效性。     

解一般线性规划逆问题的一个O（n^3L）算法

本文讨论了一般线性规划逆问题在各种情况下的求解，并基于解凸二次规划的原对偶内点算法，给出了一个O（n3L）算法和一个实用算法．     

非线性最优控制问题的保辛多层次求解方法

将非线性系统的最优控制问题导向Hamilton系统,提出了求解非线性最优控制问题的保辛多层次方法．首先,以时间区段两端状态为独立变量并在区段内采用Lagrange插值近似状态和协态变量,通过对偶变量变分原理将非线性最优控制问题转化为非线性方程组的求解．然后,在保辛算法的具体实施过程中提出了多层次求解思想,以2N类算法为基础由低层次到高层次加密离散时间区段,利用Lagrange插值得到网格加密后的初始状态与协态变量作为求解非线性方程组的初值,可提高计算效率．数值算例验证了算法在求解效率与求解精度上的有效性．     

多约束非线性整数规划的一种改进的算法

多约束非线性整数规划是一类非常重要的问题,非线性背包问题是它的一类特殊而重要的问题.定义在有限整数集上极大化一个可分离非线性函数的多约束最优化问题.这类问题常常用于资源分配、工业生产及计算机网络的最优化模型中,运用一种新的割平面法来求解对偶问题以得到上界,不仅减少了对偶间隙,而且保证了算法的收敛性.利用区域割丢掉某些整数箱子,并把剩下的区域划分为一些整数箱子的并集,以便使拉格朗日松弛问题能有效求解,且使算法在有限步内收敛到最优解.算法把改进的割平面法用于求解对偶问题并与区域分割有效结合解决了多约束非线性背包问题的求解.数值结果表明了改进的割平面方法对对偶搜索更加有效.     

基于辛对偶体系的层合板自由边缘效应的分析解

在原变量——位移和其对偶变量——应力组成的辛几何空间,建立了Pipes-Pagano模型的复合材料层合板问题的辛对偶求解体系．与传统的单类变量不同,辛对偶变量有利于同时描述层间位移连续性条件和应力平衡条件．进入辛对偶体系以后,就可以应用辛对偶体系的统一解析求解方法,如分离变量和辛本征展开的方法对层合板问题进行解析分析和求解．对层合板自由边缘效应的分析求解,验证了辛对偶体系的方法对层合板问题的分析求解是十分有效的．     

两类线性双层规划的算法

根据值型线性双层规划的 Johri一般对偶的对偶性质 ,把对两类值型线性双层规划的求解问题转化为对有限个线性规划的求解问题 ,简化了双层规划的求解过程 ,给出了求解这两类值型线性双层规划的一种有效算法     

基于最钝角规则的亏基对偶单纯形Ⅰ阶段算法

对偶单纯形算法或原始对偶单纯形算法都需要一个初始对偶可行基．就此目的而言，基于最钝角行主元规则的对偶Ⅰ阶段算法非常有效[15].本文将其思想应用于亏基情形，建立一个不含比值检验的新的亏基对偶Ⅰ价段算法．初步的数值实验表明，该算法可在总体上减少运行时间和迭代次数，极具竞争性．     

第一阶段原有单纯形和对偶单纯形算法的计算比较

线性最优化广泛应用于经济与管理的各个领域.在线性规划问题的求解中,如果一个初始基本可行解没有直接给出,则常采用经典的两阶段法求解.对含有"≥"不等式约束的线性规划问题,讨论了第一阶段原有单纯形法和对偶单纯形法两种算法形式,并根据第一阶段问题的特点提出了改进的对偶单纯形枢轴准则.最后,通过大规模数值试验对两种算法进行计算比较,结果表明,改进后的对偶单纯形算法在计算效率上明显优于原有单纯形算法.     

Two-Stage Stochastic Variational Inequality Arising from Stochastic Programming

We consider a two-stage stochastic variational inequality arising from a general convex two-stage stochastic programming problem, where the random variables have continuous distributions. The equivalence between the two problems is shown under some moderate conditions, and the monotonicity of the two-stage stochastic variational inequality is discussed under additional conditions. We provide a discretization scheme with convergence results and employ the progressive hedging method with double parameterization to solve the discretized stochastic variational inequality. As an application, we show how the water resources management problem under uncertainty can be transformed from a two-stage stochastic programming problem to a two-stage stochastic variational inequality, and how to solve it, using the discretization scheme and the progressive hedging method with double parameterization.

     

Applying oracles of on-demand accuracy in two-stage stochastic programming – A computational study

Traditionally, two variants of the L-shaped method based on Benders’ decomposition principle are used to solve two-stage stochastic programming problems: the aggregate and the disaggregate version. In this study we report our experiments with a special convex programming method applied to the aggregate master problem. The convex programming method is of the type that uses an oracle with on-demand accuracy. We use a special form which, when applied to two-stage stochastic programming problems, is shown to integrate the advantages of the traditional variants while avoiding their disadvantages. On a set of 105 test problems, we compare and analyze parallel implementations of regularized and unregularized versions of the algorithms. The results indicate that solution times are significantly shortened by applying the concept of on-demand accuracy.     

Two-stage stochastic optimization problems with stochastic ordering constraints on the recourse

We consider two-stage risk-averse stochastic optimization problems with a stochastic ordering constraint on the recourse function. Two new characterizations of the increasing convex order relation are provided. They are based on conditional expectations and on integrated quantile functions: a counterpart of the Lorenz function. We propose two decomposition methods to solve the problems and prove their convergence. Our methods exploit the decomposition structure of the risk-neutral two-stage problems and construct successive approximations of the stochastic ordering constraints. Numerical results confirm the efficiency of the methods.     

A gaussian upper bound for gaussian multi-stage stochastic linear programs

This paper deals with two-stage and multi-stage stochastic programs in which the right-hand sides of the constraints are Gaussian random variables. Such problems are of interest since the use of Gaussian estimators of random variables is widespread. We introduce algorithms to find upper bounds on the optimal value of two-stage and multi-stage stochastic (minimization) programs with Gaussian right-hand sides. The upper bounds are obtained by solving deterministic mathematical programming problems with dimensions that do not depend on the sample space size. The algorithm for the two-stage problem involves the solution of a deterministic linear program and a simple semidefinite program. The algorithm for the multi-stage problem invovles the solution of a quadratically constrained convex programming problem.     

A simple randomised algorithm for convex optimisation

We consider maximising a concave function over a convex set by a simple randomised algorithm. The strength of the algorithm is that it requires only approximate function evaluations for the concave function and a weak membership oracle for the convex set. Under smoothness conditions on the function and the feasible set, we show that our algorithm computes a near-optimal point in a number of operations which is bounded by a polynomial function of all relevant input parameters and the reciprocal of the desired precision, with high probability. As an application to which the features of our algorithm are particularly useful we study two-stage stochastic programming problems. These problems have the property that evaluation of the objective function is #P-hard under appropriate assumptions on the models. Therefore, as a tool within our randomised algorithm, we devise a fully polynomial randomised approximation scheme for these function evaluations, under appropriate assumptions on the models. Moreover, we deal with smoothing the feasible set, which in two-stage stochastic programming is a polyhedron.     

动态 Stackelberg 诱导对策问题

Stackelberg 诱导(Incentive)对策,以简单的二人对策来说,是讨论对一个具有递阶决策结构的系统,处于领导地位的决策者,如何通过选择和宣布适当的策略,来诱导处于随从地位的决策者采取对领导最为有利行动的问题.自从文[1]从控制理论的观点讨论了 Incentive 的概念后,Stackelberg 诱导对策的研究受到了较多的关注,发现了不少研究成果.然而从到迄今所出现的文献来看,对概念性问题的研究较多,而较缺乏有效的,特     

Two-Stage Image Segmentation Scheme Based on Inexact Alternating Direction Method

Image segmentation is a fundamental problem in both image processing and computer vision with numerous applications. In this paper, we propose a two-stage image segmentation scheme based on inexact alternating direction method. Specifically, we first solve the convex variant of the Mumford-Shah model to get the smooth solution, and the segmentation is then obtained by applying the K-means clustering method to the solution. Some numerical comparisons are arranged to show the effectiveness of our proposed schemes by segmenting many kinds of images such as artificial images, natural images, and brain MRI images.     

A copositive approach for two-stage adjustable robust optimization with uncertain right-hand sides

We study two-stage adjustable robust linear programming in which the right-hand sides are uncertain and belong to a convex, compact uncertainty set. This problem is NP-hard, and the affine policy is a popular, tractable approximation. We prove that under standard and simple conditions, the two-stage problem can be reformulated as a copositive optimization problem, which in turn leads to a class of tractable, semidefinite-based approximations that are at least as strong as the affine policy. We investigate several examples from the literature demonstrating that our tractable approximations significantly improve the affine policy. In particular, our approach solves exactly in polynomial time a class of instances of increasing size for which the affine policy admits an arbitrarily large gap.     

A soft approach for hard continuous optimization

This paper is to introduce a soft approach for solving continuous optimizations models where seeking an optimal solution is theoretically or practically impossible.We first review methods for solving continuous optimization models, and argue that only a few optimization models with some good structure are solved. To solve a larger class of optimization problems, we suggest a soft approach by softening the goal in solving a model, and propose a two-stage process for implementing the soft approach. Furthermore, we offer an algorithm for solving optimization models with a convex feasible set, and verify the validity of the soft approach with numerical experiments.     

构造上正交各向异性半球形凸起凹凸板等效刚度研究

根据半球形凹凸板周期性将其划分得到代表性体元结构.首先研究代表性体元的刚度特性,利用变形等效原理、均质化和刚度组合法得到半球形凹凸板的等效刚度.然后将得到的三个主向刚度代入四边简支板Navier解中求解板中心挠度.通过有限元数值模拟解和Navier解进行对比分析,从而验证该文得到的主向刚度的准确性.然后讨论了代表性体元的材料尺寸对所得等效刚度的影响.随着代表性体元边长与凸起半径的比值逐渐增大,所得结果精度越来越高,且等效刚度公式适用于不同厚度的半球形凹凸板.最后给出了较为简洁的工程应用公式,并给出了凸起半径的近似取值范围和工程应用算例.