Computing equilibria via nonconvex programming

The problem of determining a vector that places a system in a state of equilibrium is studied with the aid of mathematical programming. The approach derives from the logical equivalence between the general equilibrium problem and the complementarity problem, the latter being explicitly concerned with finding a point in the set S = {x: < x, g(x)> = 0, g(x) ≦ 0, x ≧ 0}. An associated nonconvex program, min{? < x, g(x) > : g(x) ≦ 0, x ≧ 0}, is proposed whose solution set coincides with S. When the excess demand function g(x) meets certain separability conditions, equilibrium solutions are obtained by using an established branch and bound algorithm. Because the best upper bound is known at the outset, an independent check for convergence can be made at each iteration of the algorithm, thereby greatly increasing its efficiency. A number of examples drawn from economic and network theory are presented in order to demonstrate the computational aspects of the approach. The results appear promising for a wide range of problem sizes and types, with solutions occurring in a relatively small number of iterations.     

A method for quadratic programming

A solution to the quadratic programming is presented with the constraint of the form Ax ≥ b using the linear complementary problem approach.     

An interactive branch-and-bound algorithm for bicriterion nonconvex/mixed integer programming

Although there has been extensive research on interactive multiple objective decision making in the last two decades, there is still a need for specialized interactive algorithms that exploit the relatively simple structure of bicriterion programming problems. This article develops an interactive branch-and-bound algorithm for bicriterion nonconvex programming problems. The algorithm searches among only the set of nondominated solutions since one of them is a most preferred solution that maximizes the overall value function of the decision maker over the set of achievable solutions. The interactive branch-and-bound algorithm requires only pairwise preference comparisons from the decision maker. Based on the decision maker's responses, the algorithm reduces the set of nondominated solutions and terminates with his most preferred nondominated solution. Branching corresponds to dividing the subset of nondominated solutions considered at a node into two subsets. The incumbent solution is updated based on the preference of the decision maker between two nondominated solutions. Fathoming decisions are based on the decision maker's preference between the incumbent solution and the ideal solution of the node in consideration.     

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.     

二次规划法优化设计的应用研究

本文对二次规划法进行应用研究。通过化铣网格加劲壳的二次规划法优化设计和几何规划对偶问题的二次规划解法,提出变量替换梯移法调参改善初始展开点、限制步长法和改进的高斯消元法等措施,给二次规划法的应用提供一些简单有效的方法。计算结果表明,上述处理方法在加快收敛、提高计异精度方面也收到了较好的效果。     

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.     

球约束凸二次规划的一个算法

针对球约束凸二次规划问题,利用Lagrange对偶将其转化为无约束优化问题,然后运用单纯形法对其求解,获得原问题的最优解。最后,对文中给出的算法给出了论证。     

A nonconvex max-min problem

An algorithm designed to solve a large class of nonconvex max-min problems is described. Its usefulness and applicability is demonstrated by solving an extension of a recently introduced model which optimally allocates strategic weapon systems. The extended model is shown to be equivalent to a nonconvex mathematical program with an infinite number of constraints, and hence is not solvable by conventional procedures. An example is worked out in detail to illustrate the algorithm.     

平面杆系结构内力包络图仿真优化与编程

讨论受任意集中力、集中力偶和线性分布力等复杂移动荷载作用下的平面杆系结构内力包络图仿真优化技术。借助平面杆系结构分析软件Vsap2011,分析了单元截面离散方式和荷载移动步长对内力包络图求解精度的影响。研究表明:为了获得较高的内力包络图求解精度,对于移动荷载经过的单元,截面必须按步细分;对于活载任意布置的单元,截面必须在荷载作用点之间再插入足够多的分点;对于不受荷载的单元,两端刚结的必须按距离细分,而含有一个以上铰结点的不必细分。此外,在步长接近或相差不大的情况下,如果步长既能整除荷载队列的间距,又能整除载荷载通过单元的长度,那么就可以提高内力包络图的求解精度。如果将单元长度和荷载队列间距的最大公约数作为步长,既可获得较高的内力包络图求解精度,又可大大节省计算机时。     

Tree-search algorithms for quadratic assignment problems

Problems having the mathematical structure of a quadratic assignment problem are found in a diversity of contexts: by the economist in assigning a number of plants or indivisible operations to a number of different geographical locations; by the architect or indusatrial engineer in laying out activities, offices, or departments in a building; by the human engineer in arranging the indicators and controls in an operators control room; by the electronics engineer in laying out components on a backboard; by the computer systems engineer in arranging information in drum and disc storage; by the production scheduler in sequencing work through a production facility; and so on. In this paper we discuss several types of algorithms for solving such problems, presenting a unifying framework for some of the existing algorithms, and dcscribing some new algorithms. All of the algorithms discussed proceed first to a feasible solution and then to better and better feasible solutions, until ultimately one is discovered which is shown to be optimal.     

Dynamic programming approach to the optimization of Naval aircraft rework and replacement policies

This paper describes a method for determining optimal repair and replacement policies for aireraft, with specific reference to the F–4. The objective of the analysis is to choose the set of policies from all possible alternatives over a finite planning horizon which minimizes the cost of operations. A dynamic program is presented which seeks an optimal path through a series of decision periods, when each period begins with the choice of keeping an aircraft, reworking it before further operation, or buying a new one. We do not consider changes in technology. Therefore, when a replacement does occur, it is made with a similar aircraft. Multivariate statistical techniques are used to estimate the relevant costs as a function of age, and time since last rework.     

基于Matlab优化工具箱和VB语言的消磁电流决策

针对舰船消磁电流决策问题,利用Matlab优化工具箱对舰船模型进行了消磁实验,取得了良好的消磁效果.还应用VisualBasic语言与Matlab混合编程,开发了实用的消磁电流决策软件,能够提高消磁电流的决策效率.     

A mathematical programming approach to identification and optimization of a class of unknown systems

There exists a class of decision problems for which: (1) models of input-output response functions are not available in a closed-form, functional representation; (2) informational costs associated with learning about the response function are significant. For these problems, combining identification with optimization using mathematical programming is potentially attractive. Three approaches to the identification-optimization problem are proposed: an outer-linearized approximation using relaxation (OLR); an inner-linearized approximation using restriction (ILR); and a sequential combination of inner- and outer-linearized subproblems (SIO). Algorithms based on each approach are developed and computational experience reported.     

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.     

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.     

New purification algorithms for linear programming

Two new algorithms are presented for solving linear programs which employ the opposite-sign property defined for a set of vectors in m space. The first algorithm begins with a strictly positive feasible solution and purifies it to a basic feasible solution having objective function value no less under maximization. If this solution is not optimal, then it is drawn back into the interior with the same objective function value, and a restart begins. The second algorithm can begin with any arbitrary feasible point. If necessary this point is purified to a basic feasible solution by dual-feasibility–seeking directions. Should dual feasibility be attained, then a duality value interval is available for estimating the unknown objective function value. If at this juncture the working basis is not primal feasible, then further purification steps are taken tending to increase the current objective function value, while simultaneously seeking another dual feasible solution. Both algorithms terminate with an optimal basic solution in a finite number of steps.     

向量巨型机高效程序设计技术

本文着重研究适应向量巨型机体系结构的高效程序设计技术。围绕减少指令流水线阻塞,提高多功能部件并行度,数据调度与局部化技术三个方面进行了深入的讨论,给出了一系列设计高效程序的方法和技巧。     

The conjugate gradient technique for certain quadratic network problems

We consider a class of network flow problems with pure quadratic costs and demonstrate that the conjugate gradient technique is highly effective for large-scale versions. It is shown that finding a saddle point for the Lagrangian of an m constraint, n variable network problem requires only the solution of an unconstrained quadratic programming problem with only m variables. It is demonstrated that the number of iterations for the conjugate gradient algorithm is substantially smaller than the number of variables or constraints in the (primal) network problem. Forty quadratic minimum-cost flow problems of various sizes up to 100 nodes are solved. Solution time for the largest problems (4,950 variables and 99 linear constraints) averaged 4 seconds on the CBC Cyber 70 Model 72 computer.     

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.