Quadratic programming with quadratic constraints

A program with a quadratic objective function and quadratic constraints is considered. Two duals to such programs are provided, and an algorithm is presented based upon approximations to the duals. The algorithm consists of a sequence of linear programs and programs involving the optimization of a quadratic function either unconstrained or constrained to the nonnegative orthant. An example involving production planning is presented.     

Solving quadratic assignment problems with rectangular distances and integer programming

The problem considered involves the assignment of n facilities to n specified locations. Each facility has a given nonnegative flow from each of the other facilities. The objective is to minimize the sum of transportation costs. Assume these n locations are given as points on a two-dimensional plane and transportation costs are proportional to weighted rectangular distances. Then the problem is formulated as a binary mixed integer program. The number of integer variables (all binary) involved equals the number of facilities squared. Without increasing the number of integer variables, the formulation is extended to include “site costs” Computational results of the formulation are presented.     

A decomposition procedure for convex quadratic programs

This article deals with the solution of convex quadratic programs by iteratively solving a master problem and a subproblem as proposed previously by Sacher. The approach has the advantage that the subproblems are linear programs so that advantage can be taken of existing schemes for solving large linear problems. At each step in solving the master problem, a closed-form solution can be specified so that the procedure is well suited for solving large quadratic programs and can take advantage of the constraint structure.     

An interactive paired comparison method for bicriterion integer programming

This article proposes an interactive paired comparison region elimination method for bicriterion integer mathematical programming problems. The new method isolates the best compromise solution by successively evaluating a pair of associated supported non-dominated solutions. The efficiency of the method is tested by solving randomly generated problems based on varying shapes of efficient frontiers. When compared with the existing branch-and-bound method, the method was effective in reducing the burden on the decision maker. © 1994 John Wiley & Sons, Inc.     

A parametric-based heuristic program for the quadratic assignment problem

In this article we study the quadratic assignment problem by embedding the actual data in a data space which satisfies an extension of the metric triangle property. This leads to simpler computations for the determination of heuristic solutions. Bounds are given for the loss of optimality which such heuristic solutions would involve in any specific instance. © 1993 John Wiley & Sons, Inc.     

A primal-dual cutting-plane algorithm for all-integer programming

The integer programming literature contains many algorithms for solving all-integer programming problems but, in general, existing algorithms are less than satisfactory even in solving problems of modest size. In this paper we present a new technique for solving the all-integer, integer programming problem. This algorithm is a hybrid (i.e., primal-dual) cutting-plane method which alternates between a primal-feasible stage related to Young's simplified primal algorithm, and a dual-infeasible stage related to Gomory's dual all-integer algorithm. We present the results of computational testing.     

线性规划的一种并行修正松弛算法

对求解线性规划问题的松弛算法进行了修正,在此基础上提出了一种基于cluster结构的并行算法,分析了算法的性能;基于曙光3000大规模并行计算机,给出了算法用于求解线性规划问题实例的实验结果.理论分析和实验结果表明,修正算法改进了松弛算法的实际性能,同时具有较好的并行性和稳定性,可用于求解此类大规模科学与工程规划问题的高性能计算.     

一种新的进化规划算法

参考高斯变异算子和柯西变异算子,提出了基于学生概率分布变异的进化规划算法。学生概率分布能够衔接高斯分布和柯西分布,其性能通过改变参数n来获得。通过仿真得到了学生概率分布变异的规律。仿真表明,基于学生概率分布变异的进化算法具有较好的性能。     

A cutting plane algorithm for the bilinear programming problem

In this paper we discuss the properties of a Bilinear Programming problem, and develop a convergent cutting plane algorithm. The cuts involve only a subset of the variables and preserve the special structure of the constraints involving the remaining variables. The cuts are deeper than other similar cuts.     

A quadratic assignment problem without column constraints

We convert a quadratic assignment problem [1] with a nonconvex objective function into an integer linear program. We then solve the equivalent integer program by a simple enumeration that produces global minima.     

A finiteness proof for modified dantzig cuts in integer programming

Let be a basic solution to the linear programming problem subject to: where R is the index set associated with the nonbasic variables. If all of the variables are constrained to be nonnegative integers and x_u is not an integer in the basic solution, the linear constraint is implied. We prove that including these “cuts” in a specified way yields a finite dual simplex algorithm for the pure integer programming problem. The relation of these modified Dantzig cuts to Gomory cuts is discussed.     

一类线性规划模型的求解方法

应用矩阵理论知识得到了一类特殊的线性规划模型的相关定理,给出了一种简便求解方法,讨论了求解方法的推广问题.     

A mutual primal-dual linear programming algorithm

A new primal-dual linear programming algorithm is exhibited. A proof is given that optimal solutions to both primal and dual problems (when such solutions exist) are found in a finite number of steps by this algorithm. A numerical example is included to illustrate the method.     

Suboptimal algorithms for the quadratic assignment problem

The idea of combining relatively simple continuous methods with discrete procedures is used for the construction of suboptimal algorithms for quadratic assignment problems. Depending on the nature of the special problem these steps may vary in complexity. The simplest procedures require minimum storage space and result in tolerable computation times. Different choices of parameters and random variations may be used in order to obtain statistical distributions of suboptimal solutions. Computational results for sample problems indicate improvements on results of Steinberg, Gilmore, and Hillier and Connors.     

A note on the optimality conditions for the bilevel programming problem

A counterexample is given to demonstrate that previously proposed necessary conditions for the bilevel programming problem are not correct. An interpretation of the difficulty is given by appealing to a “theorem of alternative” result presented in the original work.     

一类多目标模糊系数线性规划问题

讨论了一类所有系数均为模糊数的多目标线性规划问题 .通过对模糊数的比较 ,将模糊多目标线性规划模型转化为清晰的多目标模型 ,并应用一种基于线性隶属函数的模糊规划算法求其协调解 .最后给出了一个数值例子 .     

A comparison of the primal-simplex and complementary pivot methods for linear programming

A comparison of the complementary pivot method of Lemke-Howson and the more commonly used primal-simplex method for solving linear programming problems in symmetric dual form has been made. In our tests the complementary pivot method shows a definite superiority over the simplex method both with regard to the number of iterations and computation time.     

A note on integer linear fractional programming

This note consists of developing a method for enforcing additional constraints to linear fractional programs and showing its usefulness in solving integer linear fractional programs.