Pattern Search Method for Discrete <Emphasis Type="Italic">L</Emphasis><Subscript>1</Subscript>–Approximation

We propose a pattern search method to solve a classical nonsmooth optimization problem. In a deep analogy with pattern search methods for linear constrained optimization, the set of search directions at each iteration is defined in such a way that it conforms to the local geometry of the set of points of nondifferentiability near the current iterate. This is crucial to ensure convergence. The approach presented here can be extended to wider classes of nonsmooth optimization problems. Numerical experiments seem to be encouraging. This work was supported by M.U.R.S.T., Rome, Italy.     

Constraint incorporation in optimization

Numerical methods for solving constrained optimization problems need to incorporate the constraints in a manner that satisfies essentially competing interests; the incorporation needs to be simple enough that the solution method is tractable, yet complex enough to ensure the validity of the ultimate solution. We introduce a framework for constraint incorporation that identifies a minimal acceptable level of complexity and defines two basic types of constraint incorporation which (with combinations) cover nearly all popular numerical methods for constrained optimization, including trust region methods, penalty methods, barrier methods, penalty-multiplier methods, and sequential quadratic programming methods. The broad application of our framework relies on addition and chain rules for constraint incorporation which we develop here.     

Protein Conformation of a Lattice Model Using Tabu Search

We apply tabu search techniques to the problem of determining the optimal configuration of a chain of protein sequences on a cubic lattice. The problem under study is difficult to solve because of the large number of possible conformations and enormous amount of computations required. Tabu search is an iterative heuristic procedure which has been shown to be a remarkably effective method for solving combinatorial optimization problems. In this paper, an algorithm is designed for the cubic lattice model using tabu search. The algorithm has been tested on a chain of 27 monomers. Computational results show that our method outperforms previously reported approaches for the same model.     

随机模拟在电信网性能评价中的应用

本文介绍了可在微机上实现大规模电信网践模拟的实用化软件ＳＩＭＵＮＥＴ，及基所采用的无时钟事件追踪法。该软件可用规划中的电信网络和实际网路的模拟及性能评价，也可用于不同流量控制算法的评价。文中给出一个评价实例。     

设计IIR数字滤波器的遗传优化算法

提出了ＩＩＲ数字滤波器设计的遗传优化法。这是一种模拟自然遗传和达尔文进化理论的随机并行优化算法。首先，详细描述了遗传算法并给出了计算步骤，然后将遗传算法用于ＩＩＲ数字滤波器的优化设计，最后给出了模拟计算结果。     

Banach空间上算子张量积的联合谱

本文主要研究了 Banach 空间上交换算子组的张量积以及 Banach 代数的张量积中交换元组的 Taylor 联合谱,推广了 Vasilescu,F.H.及 Wrobel,V.等人结果。     

聚醚醚酮链结构与反应的模型化合物的量子化学研究

利用ＡＭ１方法对聚醚醚酮模型化合物全优化，结果为：芳环平均相互扭转角为３３．０度，桥键角１１７．０－１１８．０度，其分子结构拓扑图形表明：所有苯环（核）为平面构型，但其内角扭曲；其氢原子对苯环构型无实质性贡献，在更长链的计算中，冻结苯核与氢原子也得出满意结果，根据Ｍｕｌｌｉｋｅｎ键序与电荷讨论了醚交换与磺化反应。     

解Job－Shop调度问题的一个遗传算法

本文首先介绍了遗传算法的基本概念和流程，然后叙述了如何把Ｊｏｂ－Ｓｈｏｐ调度问题编码成为遗传算法的形式，并解释了对于实现这一算法中一些问题的考虑．最后给出了算法运行结果并对结果与算法做了总结。     

Combinatorial Optimization by Dynamic Contraction

A heuristic optimization methodology, Dynamic Contraction (DC), is introduced as an approach for solving a wide variety of hard combinatorial problems. Contraction is an operation that maps an instance of a problem to a smaller instance of the same problem. DC is an iterative improvement strategy that relies on contraction as a mechanism for escaping local minima. As a byproduct of contraction, efficiency is improved due to a reduction of problem size. Effectiveness of DC is shown through simple applications to two classical combinatorial problems: The graph bisection problem and the traveling salesman problem.     

Bivariate Free Knot Splines

We consider the least squares approximation of gridded 2D data by tensor product splines with free knots. The smoothing functional to be minimized—a generalization of the univariate Schoenberg functional—is chosen in such a way that the solution of the bivariate problem separates into the solution of a sequence of univariate problems in case of fixed knots. The resulting optimization problem is a constrained separable least squares problem with tensor product structure. Based on some ideas developed by the authors for the univariate case, an efficient method for solving the specially structured 2D problem is proposed, analyzed and tested on hand of some examples from the literature.