拉曼光谱在水质分析中的应用:展望及系统设计

拉曼散射光谱是一种研究物质结构强有力的工具,目前已经逐渐应用于许多领域,但在水质分析方面的应用讨论尚少。本文首先对拉曼光谱应用于水质分析的可行性及优越性进行了分析,并在此基础上设计了一套新型的应用微机和进经神经网络实现的拉曼光谱水质分析系统,以达到对水质进行高效、准确的定性和定量分析的目的。     

The linear ordering problem: Instances,search space analysis and algorithms

The linear ordering problem is an NP-hard problem that arises in a variety of applications. Due to its interest in practice, it has received considerable attention and a variety of algorithmic approaches to its solution have been proposed. In this paper we give a detailed search space analysis of available benchmark instance classes that have been used in various researches. The large fitness-distance correlations observed for many of these instances suggest that adaptive restart algorithms like iterated local search or memetic algorithms, which iteratively generate new starting solutions for a local search based on previous search experience, are promising candidates for obtaining high performing algorithms. We therefore experimentally compared two such algorithms and the final experimental results suggest that, in particular, the memetic algorithm is a new state-of-the-art approach to the linear ordering problem.     

Local simulation algorithms for Coulombic interactions

We consider a problem in dynamically constrained Monte Carlo dynamics and show that this leads to the generation of long ranged effective interactions. This allows us to construct a local algorithm for the simulation of charged systems without ever having to evaluate pair potentials or solve the Poisson equation. We discuss a simple implementation of a charged lattice gas as well as more elaborate off-lattice versions of the algorithm. There are analogies between our formulation of electrostatics and the bosonic Hubbard model in the phase approximation. Cluster methods developed for this model further improve the efficiency of the electrostatics algorithm.     

Approximate String Matching Using Deformed Fuzzy Automata: A Learning Experience

Deformed fuzzy automata are complex structures that can be used for solving approximate string matching problems when input strings are composed by fuzzy symbols. Different string similarity definitions are obtained by the appropriate selection of fuzzy operators and parameters involved in the calculus of the automaton transitions. In this paper, we apply a genetic algorithm to adjust the automaton parameters for selecting the ones best fit to a particular application. This genetic approach overcomes the difficulty of using common optimizing techniques like gradient descent, due to the presence of non-derivable functions in the calculus of the automaton transitions. Experimental results, obtained in a text recognition experience, validate the proposed methodology.     

Block Algorithms for Orthogonal Symplectic Factorizations

On the basis of a new WY-like representation block algorithms for orthogonal symplectic matrix factorizations are presented. Special emphasis is placed on symplectic QR and URV factorizations. The block variants mainly use level 3 (matrix-matrix) operations that permit data reuse in the higher levels of a memory hierarchy. Timing results show that our new algorithms outperform standard algorithms by a factor 3–4 for sufficiently large problems.This revised version was published online in October 2005 with corrections to the Cover Date.     

A conjugate-direction method based on a nonquadratic model

A conjugate-gradient method for unconstrained optimization, which is based on a nonquadratic model, is proposed. The technique has the same properties as the Fletcher-Reeves algorithm when applied to a quadratic function. It is shown to be efficient when tried on general functions of different dimensionality.     

基于遗传算法的企业风险管理组合模型的分析

本提出了企业关键风险体的概念，建立了基于关联成本和关联收益的风险管理组合净收益模型。并通过一个典型算例详细说明企业风险管理组合选择的标准和过程。实验结果表明该方法具有简单快速准确等特点，对企业的风险管理和决策具有一定的帮助和指导意义。     

Multi-Item, Multi-Facility Supply Chain Planning: Models, Complexities, and Algorithms

We propose a planning model for products manufactured across multiple manufacturing facilities sharing similar production capabilities. The need for cross-facility capacity management is most evident in high-tech industries that have capital-intensive equipment and a short technology life cycle. We propose a multicommodity flow network model where each commodity represents a product and the network structure represents manufacturing facilities in the supply chain capable of producing the products. We analyze in depth the product-level (single-commodity, multi-facility) subproblem when the capacity constraints are relaxed. We prove that even the general-cost version of this uncapacitated subproblem is NP-complete. We show that there exists an optimization algorithm that is polynomial in the number of facilities, but exponential in the number of periods. We further show that under special cost structures the shortest-path algorithm could achieve optimality. We analyze cases when the optimal solution does not correspond to a source-to-sink path, thus the shortest path algorithm would fail. To solve the overall (multicommodity) planning problem we develop a Lagrangean decomposition scheme, which separates the planning decisions into a resource subproblem, and a number of product-level subproblems. The Lagrangean multipliers are updated iteratively using a subgradient search algorithm. Through extensive computational testing, we show that the shortest path algorithm serves as an effective heuristic for the product-level subproblem (a mixed integer program), yielding high quality solutions with only a fraction (roughly 2%) of the computer time.     

Enumerating maximal independent sets with applications to graph colouring

We give tight upper bounds on the number of maximal independent sets of size k (and at least k and at most k) in graphs with n vertices. As an application of the proof, we construct improved algorithms for graph colouring and computing the chromatic number of a graph.