An Arbitrary Starting Variable Dimension Algorithm for Computing an Integer Point of a Simplex

An arbitrary starting variable dimension algorithm is proposed to compute an integer point of an n-dimensional simplex. It is based on an integer labeling rule and a triangulation of Rⁿ. The algorithm consists of two interchanging phases. The first phase of the algorithm is a variable dimension algorithm, which generates simplices of varying dimensions,and the second phase of the algorithm forms a full-dimensional pivoting procedure, which generates n-dimensional simplices. The algorithm varies from one phase to the other. When the matrix defining the simplex is in the so-called canonical form, starting at an arbitrary integer point, the algorithm within a finite number of iterations either yields an integer point of the simplex or proves that no such point exists.     

不恢复余数法的等效变形—阵列除法器的数学分析(Ⅱ)

运算器对于CPU的性能有重要影响,除法器是运算器的一个重要组件.除法器电路常用不恢复余数法,但声称采用了不恢复余数法的各种电路采用的算法却有明显区别.及其前导文试图对不恢复余数法和不恢复余数阵列除法器电路进行分析.前导给出了不恢复余数法的一种初始形式,将给出初始形式的等效变形并展示阵列除法器电路是这种等效算法的硬件实现.     

一种改进的禁忌搜索算法及其在连续全局优化中的应用

禁忌搜索算法是一种元启发式的全局优化算法,是局部搜索算法的一种推广,已被成功地应用于许多组合优化问题中。本文针对有界闭区域上的连续函数全局优化问题,提出了一种改进的禁忌搜索算法,并进行了理论分析和数值实验。数值实验表明,对于连续函数全局优化问题的求解该算法是可行有效的,并且结构简单,迭代次数较少,是一种较好的全局启发式优化算法。     

Correctness proof of an in-place permutation

The correctness of an in-place permutation algorithm is proved. The algorithm exchanges elements belonging to a permutation cycle. A suitable assertion is constructed from which the correctness can be deduced after completion of the algorithm.An in-place rectangular matrix transposition algorithm is given as an example.     

Finding feasible vectors of Edmonds-Giles polyhedra

A combinatorial algorithm for finding a feasible vector of the Edmonds-Giles polyhedron is presented. The algorithm is polynomially bounded provided that an oracle is available for minimizing submodular functions. A feasibility theorem is also proved by the algorithm and, as a consequence, a good algorithm for finding an integer-valued modular function between a sub- and a supermodular function is deduced. An important idea in the algorithm is due to Schönsleben and Lawler and Martel: the shortest augmenting paths have to be chosen in a lexicographic order.     

New Algorithm for Computing LQ Suboptimal Output Feedback Gains of Decentralized Control Systems

In this paper, the problem of computing the suboptimal output feedback gains of decentralized control systems is investigated. First, the problem is formulated. Then, the gradient matrices based on the index function are derived and a new algorithm is established based on some nice properties. This algorithm shows that a suboptimal gain can be computed by solving several ordinary differential equations (ODEs). In order to find an initial condition for the ODEs, an algorithm for finding a stabilizing output feedback gain is exploited, and the convergence of this algorithm is discussed. Finally, an example is given to illustrate the proposed algorithm.     

An alogrithm for monotonic global optimization problems ∗

We propose an algorithm to locate a global maximum of an increasing function subject to an increasing constraint on the cone of vectors with nonnegative coordinates. The algorithm is based on the outer approximation of the feasible set. We eastablish the con vergence of the algorithm and provide a number of numerical experiments. We also discuss the types of constraints and objective functions for which the algorithm is best suited     

Toward Optimal ε-Approximate Nearest Neighbor Algorithms

We combine the recent optimal predecessor algorithm with a recent randomized stratified tree algorithm for an ε-approximate nearest neighbor to give an algorithm for an ε-approximate nearest neighbor in a fixed-dimensional space that is optimal with respect to universe size. We also give a deterministic version of the stratified tree algorithm.     

A restart algorithm for computing fixed points without an extra dimension

An algorithm to compute a fixed point of an upper semicontinuous point to set mapping using a simplicial subdivision is introduced. The new element of the algorithm is that for a given grid it does not start with a subsimplex but with one (arbitrary) point only; the algorithm will terminate always with a subsimplex. This subsimplex yields an approximation of a fixed point and provides the starting point for a finer grid. Some numerical results suggest that this algorithm converges more rapidly than the known algorithms. Moreover, it is very simple to implement the algorithm on the computer.     

Novel algorithm for reconstruction of a distribution by fitting its first-four statistical moments

This paper proposes a novel algorithm to reconstruct an unknown distribution by fitting its first-four moments to a proper parametrized probability distribution (PPD) model. First, a PPD system containing three previously developed PPD models is suggested to approximate the unknown distribution, rather than empirically adopting a single distribution model. Then, a two-step algorithm based on the moments matching criterion and the maximum entropy principle is proposed to specify the appropriate (final) PPD model in the system for the distribution. The proposed algorithm is first verified by approximating several commonly used analytical distributions, along with a set of real dataset, where the existing measures are also employed to demonstrate the effectiveness of the proposed two-step algorithm. Further, the effectiveness of the algorithm is demonstrated through an application to three typical moments-based reliability problems. It is found that the proposed algorithm is a robust tool for selecting an appropriate PPD model in the system for recovering an unknown distribution by fitting its first-four moments.     

A successful algorithm for the undirected Hamiltonian path problem

In this paper a polynomial algorithm called the Minram algorithm is presented which finds a Hamiltonian Path in an undirected graph with high frequency of success for graphs up to 1000 nodes. It first reintroduces the concept described in [13] and then explains the algorithm. Computational comparison with the algorithm by Posa [10] is given.It is shown that a Hamiltonian Path is a spanning arborescence with zero ramification index. Given an undirected graph, the Minram algorithm starts by finding a spanning tree which defines a unique spanning arborescence. By suitable pivots it locates a locally minimal value of the ramification index. If this local minima corresponds to zero ramification index then the algorithm is considered to have ended successfully, else a failure is reported.Computational performance of the algorithm on randomly generated Hamiltonian graphs is given. The random graphs used as test problems were generated using the procedure explained in Section 6.1. Comparison with our version of the Posa algorithm which we call Posa-ran algorithm [10] is also made.     

Multiobjective linear programming with context-dependent preferences

Multiobjective linear programming algorithms are typically based on value maximization. However, there is a growing body of experimental evidence showing that decision maker behavior is inconsistent with value maximization. Tversky and Simonson provide an alternative model for problems with a discrete set of choices. Their model, called the componential context model, has been shown to capture observed decision maker behavior. In this paper, an interactive multiobjective linear programming algorithm is developed which follows the rationale of Tversky and Simonson. The algorithm is illustrated with an example solved using standard linear programming software. Finally, an interactive decision support system based on this algorithm is developed to field test the usefulness of the algorithm. Results show that this algorithm compares favorably with an established algorithm in the field.     

A modified method for constructing efficient solutions structure of MOLP

This paper deals with a recently proposed algorithm for obtaining all weak efficient and efficient solutions in a multi objective linear programming (MOLP) problem. The algorithm is based on solving some weighted sum problems, and presents an easy and clear solution structure. We first present an example to show that the algorithm may fail when at least one of these weighted sum problems has not a finite optimal solution. Then, the algorithm is modified to overcome this problem. The modified algorithm determines whether an efficient solution exists for a given MOLP and generates the solution set correctly (if exists) without any change in the complexity.     

线性分式多乘积规划问题的完全多项式时间近似算法

本文针对线性分式多乘积规划问题,通过Charnes-Cooper转化将原问题转化为一个等价问题,借助此等价问题提出一个获得原问题全局近似最优解的算法,最终证明了算法的收敛性,且提供了算法运算时间的理论分析.     

A sequentially optimal algorithm for numerical integration

For the class of functions of one variable, satisfying the Lipschitz condition with a fixed constant, an optimal passive algorithm for numerical integration (an optimal quadrature formula) has been found by Nikol'skii. In this paper, a sequentially optimal algorithm is constructed; i.e., the algorithm on each step makes use in an optimal way of all relevant information which was accumulated on previous steps. Using the algorithm, it is necessary to solve an integer program at each step. An effective algorithm for solving these problems is given.The author is indebted to Professor S. E. Dreyfus, Department of Industrial Engineering and Operations Research, University of California, Berkeley, California, for his helpful attention to this paper.     

基于遗传算法与牛顿法的联合算法在地表发射率反演中的应用

结合遗传算法全局高效搜索和牛顿法局部细致搜索的优势,充分利用一种算法的优点弥补另一种算法的不足,进而引入一种基于遗传算法和牛顿法的联合算法,并将联合算法应用于反演地表发射率的函数关系中.结果表明,联合算法中由遗传算法提供的初始值使得牛顿法下降的速度快,且很快趋于稳定,达到精度要求;而由任意初始值提供给牛顿法,目标函数下降到一定阶段后反而有所回升,然后才保持稳定,且经和联合算法迭代相同的次数后,目标函数的值仍然非常大,远远达不到要求.因此,从可行性、计算效率上看,联合算法均优于单纯的牛顿法,是一种性能稳定,计算高效的下降方法.     

An Adaptive Interacting Wang–Landau Algorithm for Automatic Density Exploration

While statisticians are well-accustomed to performing exploratory analysis in the modeling stage of an analysis, the notion of conducting preliminary general-purpose exploratory analysis in the Monte Carlo stage (or more generally, the model-fitting stage) of an analysis is an area that we feel deserves much further attention. Toward this aim, this article proposes a general-purpose algorithm for automatic density exploration. The proposed exploration algorithm combines and expands upon components from various adaptive Markov chain Monte Carlo methods, with the Wang–Landau algorithm at its heart. Additionally, the algorithm is run on interacting parallel chains—a feature that both decreases computational cost as well as stabilizes the algorithm, improving its ability to explore the density. Performance of this new parallel adaptive Wang–Landau algorithm is studied in several applications. Through a Bayesian variable selection example, we demonstrate the convergence gains obtained with interacting chains. The ability of the algorithm’s adaptive proposal to induce mode-jumping is illustrated through a Bayesian mixture modeling application. Last, through a two-dimensional Ising model, the authors demonstrate the ability of the algorithm to overcome the high correlations encountered in spatial models. Supplemental materials are available online.     

On the modified Gram-Schmidt algorithm for weighted and constrained linear least squares problems

A framework and an algorithm for using modified Gram-Schmidt for constrained and weighted linear least squares problems is presented. It is shown that a direct implementation of a weighted modified Gram-Schmidt algorithm is unstable for heavily weighted problems. It is shown that, in most cases it is possible to get a stable algorithm by a simple modification free from any extra computational costs. In particular, it is not necessary to perform reorthogonalization.Solving the weighted and constrained linear least squares problem with the presented weighted modified Gram-Schmidt algorithm is seen to be numerically equivalent to an algorithm based on a weighted Householder-likeQR factorization applied to a slightly larger problem. This equivalence is used to explain the instability of the weighted modified Gram-Schmidt algorithm. If orthogonality, with respect to a weighted inner product, of the columns inQ is important then reorthogonalization can be used. One way of performing such reorthogonalization is described.Computational tests are given to show the main features of the algorithm.     

The Finite Element Method for Computing the Stationary Distribution of an SRBM in a Hypercube with Applications to Finite Buffer Queueing Networks

This paper proposes an algorithm, referred to as BNAfm (Brownian network analyzer with finite element method), for computing the stationary distribution of a semimartingale reflecting Brownian motion (SRBM) in a hypercube. The SRBM serves as an approximate model of queueing networks with finite buffers. Our BNAfm algorithm is based on the finite element method and an extension of a generic algorithm developed by Dai and Harrison [14]. It uses piecewise polynomials to form an approximate subspace of an infinite-dimensional functional space. The BNAfm algorithm is shown to produce good estimates for stationary probabilities, in addition to stationary moments. This is in contrast to the BNAsm algorithm (Brownian network analyzer with spectral method) of Dai and Harrison [14], which uses global polynomials to form the approximate subspace and which sometimes fails to produce meaningful estimates of these stationary probabilities. Extensive computational experiences from our implementation are reported, which may be useful for future numerical research on SRBMs. A three-station tandem network with finite buffers is presented to illustrate the effectiveness of the Brownian approximation model and our BNAfm algorithm.     

A simplex-based labelling algorithm for the linear fractional assignment problem

This paper constructs an algorithm to solve the fractional assignment problem. Algorithms that are currently used are mostly based on parametric approaches and must solve a sequence of optimization procedures. They also neglect the difficulties caused by degeneracy. The proposed algorithm performs optimization once and overcomes degeneracy. The main features of the algorithm are an effective initial heuristic approach, a simple labelling procedure and an implicit primal-dual schema. A numerical example is presented and demonstrates that the proposed algorithm is easy to apply. Computational results are compared with those from other developed methods. The results show that the proposed algorithm is efficient.