Computing Aviation Sparing Policies: Solving a Large Nonlinear Integer Program

Deployed US Navy aircraft carriers must stock a large number of spare parts to support the various types of aircraft embarked on the ship. The sparing policy determines the spares that will be stocked on the ship to keep the embarked aircraft ready to fly. Given a fleet of ten or more aircraft carriers and a cost of approximately 50 million dollars per carrier plus the cost of spares maintained in warehouses in the United States, the sparing problem constitutes a significant portion of the Navy’s resources. The objective of this work is to find a minimum-cost sparing policy that meets the readiness requirements of the embarked aircraft. This is a very large, nonlinear, integer optimization problem. The cost function is piecewise linear and convex while the constraint mapping is highly nonlinear. The distinguishing characteristics of this problem from an optimization viewpoint are that a large number of decision variables are required to be integer and that the nonlinear constraint functions are essentially “black box” functions; that is, they are very difficult (and expensive) to evaluate and their derivatives are not available. Moreover, they are not convex. Integer programming problems with a large number of variables are difficult to solve in general and most successful approaches to solving nonlinear integer problems have involved linear approximation and relaxation techniques that, because of the complexity of the constraint functions, are inappropriate for attacking this problem. We instead employ a pattern search method to each iteration of an interior point-type algorithm to solve the relaxed version of the problem. From the solution found by the pattern search on each interior point iteration, we begin another pattern search on the integer lattice to find a good integer solution. The best integer solution found across all interations is returned as the optimal solution. The pattern searches are distributed across a local area network of non-dedicated, heterogeneous computers in an office environment, thus, drastically reducing the time required to find the solution.     

LR test for random-effects covariance structure in a parallel profile model

We consider a parallel profile model which is useful in analyzing parallel growth curves of several groups. The likelihood ratio criterion for a hypothesis concerning the adequacy of a random-effects covariance structure is obtained under the parallel profile model. The likelihood ratio criterion for the hypothesis in the general one-way MANOVA model is also obtained. Asymptotic null distributions of the criteria are derived when the sample size is large. We give a numerical example of these asymptotic results.     

A comparison of two algorithms for the assignment problem

State-of-the-art computational results have shown that the shortest augmenting path (SAP) methods are more efficient than other primal-dual and primal-simplex based methods for solving the linear assignment problem on uniprocessor computers. There is, however, some controversy concerning their merits when compared with Bertsekas' auction algorithm on multiprocessor computers. In this study we investigate the performance of these competing methods on the Alliant FX/8. For each method, theoretical motivation, sources of parallelism and computational results are presented.     

松弛型并行多分裂方法解非线性方程组的安全界

本文对某些非线性方程组F(x)=0,导出了一个算法,用它可以迭代建立F(x)=0的解的紧致上、下界。算法基于某些矩阵的多分裂,因此具有自然的并行性。我们证明了趋向于解的界之收敛原则,给出了参数的收敛性区域并考察了方法的收敛速度。     

Riemannian geometry of quantum computation

A review is given of some recent developments in the differential geometry of quantum computation for which the quantum evolution is described by the special unitary unimodular group, SU(2ⁿ). Using the Lie algebra su(2ⁿ), detailed derivations are given of a useful Riemannian geometry of SU(2ⁿ), including the connection and the geodesic equation for minimal complexity quantum computations.     

A minimization version of a directed subgraph homeomorphism problem

We consider a special case of the directed subgraph homeomorphism or topological minor problem, where the host graph has a specific regular structure. Given an acyclic directed pattern graph, we are looking for a host graph of minimal height which still allows for an embedding. This problem has applications in compiler design for certain coarse-grain reconfigurable architectures. In this application domain, the task is to simultaneously schedule, bind and route a so-called data-flow graph, where vertices represent operations and arcs stand for data dependencies between the operations, given an orthogonal grid structure of reconfigurable processing elements (PEs) that have restricted communication abilities. We show that the problem of simultaneously scheduling, binding and routing is NP-complete by describing a logic engine reduction from NAE-3-SAT. This result holds even when the input graph is a directed tree with maximum indegree two. We also give a |V|^3/2-approximation algorithm. J. A. Brenner’s research supported by the DFG Research Center Matheon “Mathematics for key technologies”. J. C. van der Veen’s research supported by DFG Focus Program 1148, “Reconfigurable Architectures”, Grants FE 407/8-1 and FE 407/8-2.     

一种带时间窗和车辆数目限制的车辆路线问题及其算法

本文提出了带时间窗和车辆数目限制的车辆路线问题的数学模型,针对该问题的特征构造了一种路线生成算法和禁忌搜索算法,并对Solomon提出的C1、R1、RC1类数据集给出了数值运算的结果,实验结果表明算法是有效的.     

A parallel version of the preconditioned conjugate gradient method for boundary element equations

The parallel version of precondition techniques is developed for matrices arising from the Galerkin boundary element method for two-dimensional domains with Dirichlet boundary conditions. Results were obtained for implementations on a transputer network as well as on an nCUBE-2 parallel computer showing that iterative solution methods are very well suited for a MIMD computer. A comparison of numerical results for iterative and direct solution methods is presented and underlines the superiority of iterative methods for large systems.     

最小树的一般算法

本文根据设计并行算法的基本原则,给出了最小树的两个对偶定理.在此基础上,建立了两种对偶的同步并行算法的雏型.这两种算法恰恰在对偶的意义下,概括了以往的最小树算法.