一类特殊线性双层规划的对偶规划

本文首先给出值型线性双层规划的等价形式 ,然后讨论了非增的值型线性双层规划的 Johri一般对偶规划 ,并且说明了其对偶间隙等于零 ,最后说明了它们最优解的关系     

求解弱线性双层规划问题的一种全局优化方法

双层规划在经济、交通、生态、工程等领域有着广泛而重要的应用.目前对双层规划的研究主要是基于强双层规划和弱双层规划.然而,针对弱双层规划的求解方法却鲜有研究.研究求解弱线性双层规划问题的一种全局优化方法,首先给出弱线性双层规划问题与其松弛问题在最优解上的关系,然后利用线性规划的对偶理论和罚函数方法,讨论该松弛问题和它的罚问题之间的关系.进一步设计了一种求解弱线性双层规划问题的全局优化方法,该方法的优势在于它仅仅需要求解若干个线性规划问题就可以获得原问题的全局最优解.最后,用一个简单算例说明了所提出的方法是可行的.     

基于凹性割的线性双层规划全局优化算法

通过对线性双层规划下层问题对偶间隙的讨论,定义了一种凹性割,利用该凹性割的性质,给出了一个求解线性双层规划的割平面算法。由于线性双层规划全局最优解可在其约束域的极点上达到,提出的算法能求得问题的全局最优解,并通过一个算例说明了算法的有效性。     

一类值型双层凸规划的Johri一般对偶

本文首先给出一类特殊的值型凸二次双层规划一其下层子规划只含有线性约束(简记为VBCP)；然后证明了一般形式的VBCP可以等价变换为非增值型凸二次双层规划的形式；最后给出该类双层规划VBCP的Johri对偶规划及其对偶性质．     

一种非增值型凸二次双层规划的有效算法

主要研究了非增值型凸二次双层规划的一种有效求解算法。首先利用数学规划的对偶理论,将所求双层规划转化为一个下层只有一个无约束凸二次子规划的双层规划问题.然后根据两个双层规划的最优解和最优目标值之间的关系,提出一种简单有效的算法来解决非增值型凸二次双层规划问题.并通过数值算例的计算结果说明了该算法的可行性和有效性。     

用罚函数求解线性双层规划的全局优化方法

用罚函数法将线性双层规划转化为带罚函数子项的双线性规划问题，由于其全局最优解可在约束域的极点上找到，利用对偶理论给出了一种求解该双线性规划的方法，并证明当罚因子大于某一正数时，双线性规划的解就是原线性双层规划的全局最优解。     

一种改进的双层规划内点算法(英文)

本文针对线性双层规划问题提出一个由KMY算法演变而来的原对偶内点算法.与现在很多线性双层规划单纯型算法不同,作者提出的算法从一可行初始点穿过约束多面体内部直接得到近似最优解,当约束条件和变量数目增加时,本算法的迭代次数和计算时间变化很小.所以大大提高实际可操作性能和运算效率.     

均衡约束数学规划问题的一类广义Mond-Weir型对偶理论

针对均衡约束数学规划模型难以满足约束规范及难于求解的问题,基于Mond和Weir提出的标准非线性规划的对偶形式,利用其S稳定性,建立了均衡约束数学规划问题的一类广义Mond-Weir型对偶,从而为求解均衡约束优化问题提供了一种新的方法.在Hanson-Mond广义凸性条件下,利用次线性函数,分别提出了弱对偶性、强对偶性和严格逆对偶性定理,并给出了相应证明.该对偶化方法的推广为研究均衡约束数学规划问题的解提供了理论依据.     

凸二次-线性双层规划的共轭对偶及其性质

研究具有一般形式的凸二次-线性双层规划问题。讨论了这类双层规划问题的DC规划等价形式,利用DC规划共轭对偶理论,提出了凸二次-线性双层规划的共轭对偶规划,并给出相应的对偶性质。     

非光滑伪线性多目标规划对偶

本文利用Ｄｉｎｉ右上、右下导数给出了非光滑伪线性多目标规划的对偶理论，建立了Ｍｏｎｄ－Ｗｅｉｒ型对仍与Ｗｏｌｆ型对偶；并证明了原问题与对偶问题之间的对偶定理．     

Differential-Algebraic Approach to Linear Programming

This paper presents a differential-algebraic approach for solving linear programming problems. The paper shows that the differential-algebraic approach is guaranteed to generate optimal solutions to linear programming problems with a superexponential convergence rate. The paper also shows that the path-following interior-point methods for solving linear programming problems can be viewed as a special case of the differential-algebraic approach. The results in this paper demonstrate that the proposed approach provides a promising alternative for solving linear programming problems.     

解线性目标规划的《量化优先因子法》—基于LP过程的算法

本文提出一种解线性目标规划及整数线性目标规划的《量化优先因子法》,即把目标规划中表示优先等级的优先因子 pl( l=1 ,2 ,… ,L)用能从数量级上刻划优先因子 Pl Pl+ 1的本质特征的数来表示 ,进而用 SAS/OR软件包中解线性规划的 LP过程即可求解此线性目标规划 .通过实例给出算法与用 LP过程求解的程序 .     

Algorithms for highly symmetric linear and integer programs

This paper deals with exploiting symmetry for solving linear and integer programming problems. Basic properties of linear representations of finite groups can be used to reduce symmetric linear programming to solving linear programs of lower dimension. Combining this approach with knowledge of the geometry of feasible integer solutions yields an algorithm for solving highly symmetric integer linear programs which only takes time which is linear in the number of constraints and quadratic in the dimension.     

A Simplified Novel Technique for Solving Fully Fuzzy Linear Programming Problems

This study proposes a novel technique for solving Linear Programming Problems in a fully fuzzy environment. A modified version of the well-known simplex method is used for solving fuzzy linear programming problems. The use of a ranking function together with the Gaussian elimination process helps in solving linear programming problems in a fully uncertain environment. The proposed algorithm is flexible, easy and reasonable.     

Linear complementarity problems and multiple objective programming

An equivalence is demonstrated between solving a linear complementarity problem with general data and finding a certain subset of the efficient points of a multiple objective programming problem. A new multiple objective programming based approach to solving linear complementarity problems is presented. Results on existence, uniqueness and computational complexity are included.     

Using complementary slackness property to solve linear programming with fuzzy parameters

The most popular approach to handle the challenge of solving fuzzy linear programming problems is to convert the fuzzy linear programming into the corresponding deterministic linear programming. Mahdavi-Amiri and Nasseri [15,16] developed the fuzzy dual simplex algorithm to fuzzy linear programming with fuzzy parameters. In this paper, we use the complementary slackness to solve it without the need of a simplex tableau.     

Extensions of Dinkelbach's algorithm for solving non-linear fractional programming problems

Dinkelbach's algorithm was developed to solve convex fractinal programming. This method achieves the optimal solution of the optimisation problem by means of solving a sequence of non-linear convex programming subproblems defined by a parameter. In this paper it is shown that Dinkelbach's algorithm can be used to solve general fractional programming. The applicability of the algorithm will depend on the possibility of solving the subproblems. Dinkelbach's extended algorithm is a framework to describe several algorithms which have been proposed to solve linear fractional programming, integer linear fractional programming, convex fractional programming and to generate new algorithms. The applicability of new cases as nondifferentiable fractional programming and quadratic fractional programming has been studied. We have proposed two modifications to improve the speed-up of Dinkelbachs algorithm. One is to use interpolation formulae to update the parameter which defined the subproblem and another truncates the solution of the suproblem. We give sufficient conditions for the convergence of these modifications. Computational experiments in linear fractional programming, integer linear fractional programming and non-linear fractional programming to evaluate the efficiency of these methods have been carried out.     

An unconstrained dual approach to solving Karmarkar-type linear programs using conventional barrier functions

This paper proposes an unconstrained dual approach and an efficient algorithm for solving Karmarkar-type linear programming problems. Conventional barrier functions are incorporated as a perturbation term in the derivation of the associated duality theory. An optimal solution of the original linear program can be obtained by solving a sequence of unconstrained concave programs, or be approximated by solving one such dual program with a sufficiently small perturbation parameter. A globally convergent curved-search algorithm with a quadratic rate of convergence is designed for this purpose. Based on our testing results, we find that the computational procedure is very efficient and can be a viable approach for solving linear programming problems.     

凸函数的若干新性质及应用

本文证明了凸函数的若干新性质 ,讨论了这些性质在求解线性与非线性不等式组和线性规划中的应用 ,为线性与非线性不等式组、线性规划的求解提供了一种新方法 .