油田稳产措施规划数学模型

措施规划是油田开发领域的一项极为重要的工作,它对于延长油田稳产年限,合理地安排稳产措施是十分必要的.本文对措施规划的几类数学模型进行了分析,提出了对不确定性因素的处理--随机规划的建立与求解方法.     

二层规划的神经网络解法

本文研究了基于神经网络的二层规划问题.利用互补松弛条件的扰动,获得了二层规划问题局部最优解的充分条件,克服了互补松弛条件不满足约束规格的局限性,并给出了相应的神经网络求解方法,从而求解原二层规划问题,数值实验表明算法有效.     

线性规划最优解集的表现

文献［１］讨论了有无穷多最优解的线性规划问题，并利用最优单纯形表格的检验数给出线性规划有无穷多最优解的判别法，本文利用最优基可行解的凸组合及最优极向的非负线性组合给出线性规划最优解集的表现，从而把线性规划最优解集的几何特征阐释清楚．     

一类线性乘积规划问题的分支定界缩减方法

提出了求解一类线性乘积规划问题的分支定界缩减方法, 并证明了算法的收敛性.在这个方法中, 利用两个变量乘积的凸包络技术, 给出了目标函数与约束函数中乘积的下界, 由此确定原问题的一个松弛凸规划, 从而找到原问题全局最优值的下界和可行解. 为了加快所提算法的收敛速度, 使用了超矩形的缩减策略. 数值结果表明所提出的算法是可行的.     

Generating Convex Polynomial Inequalities for Mixed 0–1 Programs

We develop a method for generating valid convex quadratic inequalities for mixed0–1 convex programs. We also show how these inequalities can be generated in the linear case by defining cut generation problems using a projection cone. The basic results for quadratic inequalities are extended to generate convex polynomial inequalities.     

Designing a majorization scheme for the recourse function in two-stage stochastic linear programming

We discuss issues pertaining to the domination from above of the second-stage recourse function of a stochastic linear program and we present a scheme to majorize this function using a simpler sublinear function. This majorization is constructed using special geometrical attributes of the recourse function. The result is a proper, simplicial function with a simple characterization which is well-suited for calculations of its expectation as required in the computation of stochastic programs. Experiments indicate that the majorizing function is well-behaved and stable.     

线性多级规划的最优性条件和基本性质

本文研究的线性多级规划模型比较一般化，容许集可以是无界的，每级的目标函数可以与各下级控制的决策变量有关．我们得到了这类多级规划的一组最优性充要条件，利用这组条件推导了各级可行集的弱拟凸性、连通性等几何性质．作为应用订正了Bard的一个例题．     

REVIEW OF DYNAMIC OPTIMIZATION METHODS IN RENEWABLE NATURAL RESOURCE MANAGEMENT

In recent years, the applications of dynamic optimization procedures in natural resource management have proliferated. A systematic review of these applications is given in terms of a number of optimization methodologies and natural resource systems. Optimization methods are characterized by (1) the mathematical model used to describe a natural resource system, (2) a set of feasible strategies available to the resource manager, and (3) an objective functional by which to measure benefits and costs of strategies. A formal statement of the control problem is used to describe six approaches to optimal utilization of renewable natural resources: variational mathematics, specifically Pontryagin's Maximum Principle; dynamic programming; linear programming; nonlinear programming; simulation-optimization; and classical procedures. Solution methodologies are illustrated for each of these approaches, and examples from the ecological and natural resource literature are described for various subject matter areas. Applications are highlighted in terms of model structures, objective functionals, and system constraints. To the extent possible, optimal management patterns are characterized. Finally, the applicability of the methods to renewable natural resource systems are compared in terms of system complexity, system size, and precision of the optimal solutions. Recommendations are made concerning the appropriate methods for certain kinds of biological resource problems.     

用随机规划方法寻求线性回归模型小样本L_1－估计量的分布

为了使Ｌ１－估计能在ｉ导手械玫接τ这里我们给出求线性回归模型小样本Ｌ１－估计量的概率分布的方法．根据Ｌ１－估计量的特殊结构和使用随机规划的方法可以省去不少计算量，从而使这一方法更加有效．     

Stochastic semidefinite programming: a new paradigm for stochastic optimization

Semidefinite programs are a class of optimization problems that have been studied extensively during the past 15 years. Semidefinite programs are naturally related to linear programs, and both are defined using deterministic data. Stochastic programs were introduced in the 1950s as a paradigm for dealing with uncertainty in data defining linear programs. In this paper, we introduce stochastic semidefinite programs as a paradigm for dealing with uncertainty in data defining semidefinite programs.The work of this author was supported in part by the U.S. Army Research Office under Grant DAAD 19-00-1-0465. The material in this paper is part of the doctoral dissertation of this author in preparation at Washington State University.     

求解随机凸规划概率约束问题的对偶算法

1引言随机规划中的概率约束问题在工程和管理中有广泛的应用.因为问题中包含非线性的概率约束,它们的求解非常困难.如果目标函数是线性的,问题的求解就比较容易.给出了一个求解随机线性规划概率约束问题的综述.原-对偶算法和切平面算法是比较有效的.在本文中,我们讨论随机凸规划概率约束问题:     

A sparse sequential quadratic programming algorithm

Described here is the structure and theory for a sequential quadratic programming algorithm for solving sparse nonlinear optimization problems. Also provided are the details of a computer implementation of the algorithm along with test results. The algorithm maintains a sparse approximation to the Cholesky factor of the Hessian of the Lagrangian. The solution to the quadratic program generated at each step is obtained by solving a dual quadratic program using a projected conjugate gradient algorithm. An updating procedure is employed that does not destroy sparsity.     

Binary fuzzy goal programming

Goal programming is an important technique for solving many decision/management problems. Fuzzy goal programming involves applying the fuzzy set theory to goal programming, thus allowing the model to take into account the vague aspirations of a decision-maker. Using preference-based membership functions, we can define the fuzzy problem through natural language terms or vague phenomena. In fact, decision-making involves the achievement of fuzzy goals, some of them are met and some not because these goals are subject to the function of environment/resource constraints. Thus, binary fuzzy goal programming is employed where the problem cannot be solved by conventional goal programming approaches. This paper proposes a new idea of how to program the binary fuzzy goal programming model. The binary fuzzy goal programming model can then be solved using the integer programming method. Finally, an illustrative example is included to demonstrate the correctness and usefulness of the proposed model.     

一类最优指派问题的动态规划模型

考虑一类指派问题：欲指派m个人去做n项工作(m≥n)，要求每个人只做一项工作，第j项工作可以由b_j个人共同去做，其中，b_j(b_j≥1)是待求的未知数，j=1,2,…,n,满足．假定已知第i人做第j项工作的效益为c_ij≥0,i=1,2,…m;j=1,2,…,n.本文建立了求解上述问题最优指派（即使总的效益最大)的动态规划模型．     

一类多目标半无限分式规划的最优性条件

<正>0引言分式规划作为最优化的一个分支,近年来,获得了很大的发展,如,文[4]利用(F,α,ρ,d)-凸函数,文[5]利用半局部预不变凸函数等分别讨论了相应的分式规划问题等,这些成果极大地推动了分式规划的发展.     

Deriving an unconstrained convex program for linear programming

Consider a linear programming problem in Karmarkar's standard form. By perturbing its linear objective function with an entropic barrier function and applying generalized geometric programming theory to it, Fang recently proposed an unconstrained convex programming approach to finding an epsilon-optimal solution. In this paper, we show that Fang's derivation of an unconstrained convex dual program can be greatly simplified by using only one simple geometric inequality. In addition, a system of nonlinear equations, which leads to a pair of primal and dual epsilon-optimal solutions, is proposed for further investigation.This work was partially supported by the North Carolina Supercomputing Center and a 1990 Cray Research Grant. The authors are indebted to Professors E. L. Peterson and R. Saigal for stimulating discussions.     

An Exact Method for Fractional Goal Programming

Goal Programming with fractional objectives can be reduced to mathematical programming with a linear objective under linear and quadratic constraints, thus optimal solutions can be obtained by using existing Global Optimization techniques. However, only heuristic procedures are suggested in the literature on the field. In this note we explore the practical applicability of a recent algorithm for nonconvex quadratic programming with quadratic constraints for this problem. Encouraging computational experiences for randomly generated instances with up to 14 fractional objectives are presented.     

关于E-凸函数及E-凸规划几个错误结论的修正

本文研究Youness在1999年建立的有关E凸函数和E规划的结论.利用E凸函数和E凸规划的基本性质和优化分析技术,获得了有关E凸函数E凸规划的几个错误结论的修正..     

调节作用下投资策略的评价

本文用实物期权的方法评价调节作用下的投资策略 ,建立期权定价模型。模型中项目价值遵循均值返回过程 ,项目价值的路径采用数值模拟分析 ,文中运用动态规划方法推出期权定价公式 ,通过数值解法求解和分析数值结果 ,得出调节作用对投资的影响。     

油田高含水期稳产措施配置多层目标规划

措施规划对于延长油田稳产年限 ,提高采油速度及提高最终采收率是十分必要的 .有些学者建立了油田稳产措施规划的整体或区块规划模型 ,但没有考虑实际油田生产各生产层系的地质特性和所采取措施的差别 .本文针对油田开发实际中存在多层现象 ,以区块的各个生产层为基础 ,建立了油田措施的多层目标规划模型 ,并采用合理的算法进行求解 .应用结果表明 ,多层目标规划使措施配置更精细 ,更能反映生产实际 ,是解决油田措施配置问题的一项有力工具