基于F-B函数的牛顿法解一般约束优化规划问题

文章给出了一个解决一般约束最优化问题的含调节参数型的牛顿算法.算法有两个重要特征,首先,算法借助Lagrange函数和NcP中的F-B函数,通过构造等价于点条件的线性方程组采处理一般约束优化问题,其次,利用F-B函数的光滑性质,定义了调节参数,从而弱化了K-T点条件.文章在适当的条件下,证明了该算法具有全局收敛性.数值实验表明算法有效.     

陈骥《文则》的现代修辞观

陈骏的《文则》是中国历史上第一部谈及文法修辞的专著，其价值是多方面的，本文从陈骏创作实践中运用多种积极辞格阐述相关的修辞理论，以评价他的积极修辞观；《文则》“虽以事出创造，不无瑕疵”，但论及了语法、句法、辞格、文章、风格、文体等多方面内容，初步建立了大修辞学体系，体现了现代修辞学界所倡导的广义修辞观；陈骏虽是对《诗》《书》《礼》《易》《春秋》等静态书面语言的考察，但用动态的辩证的眼光提出了不少真知灼见，体现了动态的辩证的修辞观。     

An embedding domain approach for a class of 2-d shape optimization problems: mathematical analysis

This contribution combines a shape optimization approach to free boundary value problems of Bernoulli type with an embedding domain technique. A theoretical framework is developed which allows to prove continuous dependence of the primal and dual variables in the resulting saddle point problems with respect to the domain. This ensures the existence of a solution of a related shape optimization problem in a sufficiently large class of admissible domains.     

Asymptotic Properties of Random Multidimensional Assignment Problems

The multidimensional assignment problem (MAP) is a NP-hard combinatorial optimization problem, occurring in many applications, such as data association. In this paper, we prove two conjectures made in Ref. 1 and based on data from computational experiments on MAPs. We show that the mean optimal objective function cost of random instances of the MAP goes to zero as the problem size increases, when assignment costs are independent exponentially or uniformly distributed random variables. We prove also that the mean optimal solution goes to negative infinity when assignment costs are independent normally distributed random variables.     

Branch and win: OR tree search algorithms for solving combinatorial optimisation problems

Currently, most combinatorial optimisation problems have to be solved, if the optimum solution is sought, using general techniques to explore the space of feasible solutions and, more specifically, through exploratory enumerative procedures in trees and search graphs. We propose Branch and Win, a general formulation for understanding and synthesising the different tree search procedures that have been presented in the literature of operations research as well as in that of artificial intelligence. Several general ideas are also presented, whose application allows designing new hybrid search algorithms, in order to implement the procedure.     

Algorithms for Finite and Semi-Infinite Min–Max–Min Problems Using Adaptive Smoothing Techniques

We develop two implementable algorithms, the first for the solution of finite and the second for the solution of semi-infinite min-max-min problems. A smoothing technique (together with discretization for the semi-infinite case) is used to construct a sequence of approximating finite min-max problems, which are solved with increasing precision. The smoothing and discretization approximations are initially coarse, but are made progressively finer as the number of iterations is increased. This reduces the potential ill-conditioning due to high smoothing precision parameter values and computational cost due to high levels of discretization. The behavior of the algorithms is illustrated with three semi-infinite numerical examples.     

On the Image Regularity Condition

In this paper, we continue the analysis of the image regularity condition (IRC) as introduced in a previous paper where we have proved that IRC implies the existence of generalized Lagrange-John multipliers with first component equal to 1. The term generalized is connected with the fact that the separation (in the image space) is not necessarily linear (when we have classic Lagrange-John multipliers), but it can be also nonlinear. Here, we prove that the IRC guarantees, also in the nondifferentiable case, the fact that 0 is a solution of the first-order homogeneized (linearized) problem obtained by means of the Dini-Hadamard derivatives.     

Finding Robust Solutions Using Local Search

This paper investigates how a local search metaheuristic for continuous optimisation can be adapted so that it finds broad peaks, corresponding to robust solutions. This is relevant in problems in which uncertain or noisy data is present. When using a genetic or evolutionary algorithm, it is standard practice to perturb solutions once before evaluating them, using noise from a given distribution. This approach however, is not valid when using population-less techniques like local search and other heuristics that use local search. For those algorithms to find robust solutions, each solution needs to be perturbed and evaluated several times, and these evaluations need to be combined into a measure of robustness. In this paper, we examine how many of these evaluations are needed to reliably find a robust solution. We also examine the effect of the parameters of the noise distribution. Using a simple tabu search procedure, the proposed approach is tested on several functions found in the literature. This revised version was published online in August 2006 with corrections to the Cover Date.     

Proper Efficiency in Vector Optimization on Real Linear Spaces

In this paper, we use an algebraic type of closure, which is called vector closure, and through it we introduce some adaptations to the proper efficiency in the sense of Hurwicz, Benson, and Borwein in real linear spaces without any particular topology. Scalarization, multiplier rules, and saddle-point theorems are obtained in order to characterize the proper efficiency in vector optimization with and without constraints. The usual convexlikeness concepts used in such theorems are weakened through the vector closure.     

大学“通才教育”背景下《无机化学》教学改革探讨

无机化学是化学及相关专业的一门重要基础课。在通才教育背景下，如何深化无机化学教学改革？本文在教学理念、教学内容、教学方法和评价体系等四个方面进行了初步的探讨。