符号几何规划的一种分解方法

针对符号几何规划提出了一种直接的分解方法,将难于求解的符号几何规划问题等价地转化为一个非线性程度很低的可分离规划,为寻求困难度高且规模较大的符号几何规划问题的求解提供了一种方法,特别是经此方法分解后的每个子问题均易于求解,最后给出了数值实例,验证了此方法的有效性．     

系数是模糊数的模糊正项几何规划的一种解法

讨论系数是模糊数的模糊正项几何规划的一种解法,利用模糊数比较方法,把系数是模糊数的正项几何规划转化为普通正项几何规划,再利用求解正项几何规划的方法有效地求解模糊正项几何规划.     

基于共形几何代数的几何分解及程序实现

本文主要讨论了利用共形几何代数来进行几何定理中的几何构型进行几何分解的算法以及它的程序实现问题.利用这个算法可以给出几何量之间的定量依赖关系.所实现的程序能够给出一些较为复杂的几何命题的自动分解的结果.     

农田最优复种的多目标分层几何规划模型及其求解

本文对具有多类复种方式的农田种植问题提出了一类多目标分层几何规划数学模型。根据模型的特点,给出了求解的方法,并把它应用于我国某水稻区农田种植方案的最优选择,取得良好的效果。     

几何规划的序列线性方程组解法

1 引 言 众所周知,几何规划是非线性规划中的一类特殊规划。数学规划的前辈,早已预料:若对几何规划能探索出有效可行的算法,必将把非线性规划,特别是分式规划的算法推向新的台阶,即任何非线性规划问题,在一定的条件下,都可用一串几何规划去逼近,正因为如此,近几年,我们对几何规划的理论和算法,再一次产生了浓厚的兴趣,获得了一些新的结果,写此文目的,是想引起更多的规划专家的注意和重视,把冷落了近二十年的几何规划,推向新的台阶,为非线性规划的求解开辟新的途径。 几何规划的一般形式为     

Fuzzy正项几何规划的分类及相应类的性质

本文将Ｆｕｚｚｙ正项几何规划分成三类，并分别探讨了其相容性与Ｆｕｚｚｙ最优解问题。     

Fuzzy正项几何规划的直接算法

将Fuzzy正项几何规划化的一变量有上、下界限制的Fuzzy正项几何规划,利用Fuzzy几何不等式,又将该变量有上、下界限制的Fuzzy正项几何规划化为单项Fuzzy正项几何规划,提出基于Fuzzy值集割平面法的两种直接算法,并通过一个数值例证实该方法的有效性。     

一类非线性不等式组的相容性及其应用

本文讨论了一类非线性不等式组的相容性,给出了其相容的充分必要条件．利用其相容性,我们分别给出了一类非线性规划的解析最优解和一类几何规划有最优解的判定条件     

无约束广义几何规划的一种最新算法

1　引　　言近十几年来 ,几何规划新的有效数值求解方法成果很少 ,但几何规划在工程中的应用却十分广泛 ,随着线性、二次规划和非线性规划的各种新的数值方法的出现 ,必将把几何规划推向新阶段 .本文充分利用广义几何规划的特点 ,根据目标函数的梯度及 Hessian阵具有简单的特殊表达式 ,再结合信赖域算法构造了一种特殊算法 ,每次迭代只需解一类特殊的线性方程组 ,并在相对弱的条件下证明了全局收敛性和局部二次收敛性 ,具有比采用一般非线性规划求解速度快、精度高、占用内存少等优点 .考虑如下无约束的广义几何规划问题minh(t) = mj=1cj n…     

最小交互熵在投入─产出表理论预测的应用

本文利用最小交互熵的概念和有关结论，直接地给出具有固定边际的投入─产出表预测模型的ＲＡＳ方法的结构形式；同时利用信息论中的不等式，直接地证明最小交互熵解就是对偶几何规划解；从而简单、明了地赋于投入─产出表ＲＡＳ方法的信息意义．我们还给出一种迭代计算方案．最后指出，该方法可以用统计方法检验新的理论预测与原投入─产出表的差异性．     

Rough posynomial geometric programming

A rough posynomial geometric programming is put forward by the author. This model is advantageous for us to consider questions not only from the quantity of aspect, but from the quality because it contains more information than a traditional geometric programming one. Here, a rough convex function concept is advanced in rough value sets on foundation of rough sets and rough convex sets. Besides, a knowledge expression model in rough posynomial geometric programming is established and so is a mathematical one. Thirdly, solution properties are studied in mathematical model of rough posynomial geometric programming, and antinomy of the more-for-less paradox is solved with an arithmetic in rough posynomial geometric programming given, which can be changed into a rough linear programming after monomial rough posynomial geometric programming is solved. Finally, validity in model and algorithm is verified by examples.     

A General Alternative Procedure for Solving Negative Degree of Difficulty Problems in Geometric Programming

The degree of difficulty is an important concept in classical geometric programming theory. The dual problem is often infeasible when the degree of difficulty is negative and little has been published on this topic. In this paper, an alternative procedure is developed to find the optimal solution for the posynomial geometric programming problem with a negative degree of difficulty. First an equivalent problem was constructed with a positive degree of difficulty and the general posynomial geometric programming problem was solved using an original method previously developed by the authors. This method avoids the difficulty of non-differentiability of the dual objective function in the classical methods classified as dual. It also avoids the problem that appears when the feasible region for the dual problem is formed by an inconsistent system of linear equations.     

混合约束下广义几何规划的一种全局收敛算法

In this paper, we develop a rapidly convergent algorithm for mixed constrained signomial geometric programming. The algorithm makes use of the characteristics of signomial geometric programming, and establishes a new active-set strategy on the basis of trust region method. The global convergence is proved, and some numerical tests are given to illustrate the effectiveness.     

Profit maximization with quantity discount: An application of geometric programming

Profit maximization is an important issue to the firms that pursue the largest economic profit possible. Traditionally, profit maximization problem is solved by differentiating with respect to input prices. The total differentiation of the first-order conditions might give complicated equations difficult to handle. Different from traditional studies, this paper considers input quantity discount and employs geometric programming technique to derive the objective value for the profit-maximization problem. The geometric programming approach not only gives the global optimum solution but also provides the information that is able to discover the relationship between profit maximization and returns to scale in the solution process. No differentiation is required. Moreover, geometric programming can provide a computationally attractive view of sensitivity analysis for the changes in parameters. Examples are given to illustrate the idea proposed in this paper.     

A set of geometric programming test problems and their solutions

This paper attempts to provide a set of standard test examples for researchers working in the area of geometric programming and general nonlinear, continuous, nonconvex programming algorithms. The examples consist partly of applications of nonlinear programming that have appeared in the literature and partly of original geometric programming applications. Solutions to all the problems are provided as well as the starting points from which these solutions were computed. Other computationally important aspects such as tolerances and degree of accuracy with which these problems were solved, are also included.This work was supported in part by Canada Council Grant #S74-0148 and National Research Council of Canada Grant #084-6319-32.     

Complementary variational principle and duality in mathematical programming

The relationship between the complementary variational principle and duality in mathematical programming is demonstrated through a geometric approach in a Hilbert space setting. A necessary and sufficient condition for the existence of such a principle is given in the case of a convex functional constrained by linear dynamics. Its relationship to the Kuhn-Tucker saddle point theory is indicated. Applications to various programming and control problems are discussed.     

An Algorithm for Approximate Multiparametric Convex Programming

For multiparametric convex nonlinear programming problems we propose a recursive algorithm for approximating, within a given suboptimality tolerance, the value function and an optimizer as functions of the parameters. The approximate solution is expressed as a piecewise affine function over a simplicial partition of a subset of the feasible parameters, and it is organized over a tree structure for efficiency of evaluation. Adaptations of the algorithm to deal with multiparametric semidefinite programming and multiparametric geometric programming are provided and exemplified. The approach is relevant for real-time implementation of several optimization-based feedback control strategies.     

基于几何布朗运动的投资组合模型

在证券的价格过程是几何布朗运动的前提下,建立了最优投资组合的多目标规划模型,使得投资收益最大和投资风险最小,并利用线性加权和法求得有效解.最后用实例进行分析.     

Reliability and cost analysis of series system models using fuzzy parametric geometric programming

In this paper, we have discussed series system models with system reliability and cost. We have considered two types of the model; the former focuses on a problem of optimal reliability for series system with cost constraint and the latter is a center system cost model with reliability goal. It is necessary to improve the reliability of the system under limited available cost of system and also to minimize the systems cost subject to target goal of the reliability. Practically, cost of components has always been imprecise with vague in nature. So they are taken as fuzzy in nature and the reliability models are formulated as a fuzzy parametric geometric programming problem. Numerical examples are given to illustrate the model through fuzzy parametric geometric programming technique.