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.     

基于指数分布的进化策略

典型的进化策略受自然进化过程的启发而成为求解全局优化问题的重要方法。传统的ES变异算子作为一个主要的进化技术是建立在正态分布的随机变量基础上的，本文提出了基于指数分布的进化策略由于采用了新的变异算子有效地减少了产生探试解的成本，从而优于传统的进化策略。     

自控式电磁线圈炮

以交流电驱动电磁场为动力，结合直流电机与磁力天平理论的新成果，设计了线圈型电磁炮.     

面阵探测器的像点亚像素定位研究

面阵探测器可直接探测点目标图像的位置，但其定位精度受到探测器分辨率的限制。因此，亚像元精度技术得到了发展。它是利用目标像点的灰度分布特性，通过内插细分算法，确定出像点位置。可将定位精度提高1—2个数量级。本文介绍了几种实现目标像点亚像素定位的内插细分算法。对它们的计算公式、特点进行了分析，并进行了实验验证。     

一种基于闭环优化的新型鲁棒预测控制方法

针对带约束的凸多面体线性不确定模型，提出了一种新型鲁棒预测控制方法，它采用离散化的不确定模型构造最小－最大优化控制问题，并在其中直接引入状态反馈机制，与其他最小－最大预测控制方法相比，这种方法等效于增加了控制序列的长度，为优化问题增加了更多的自由度，从而扩大了可行域，作为最小化目标的是离散化不确定系统在整个预测时域上二次型成本函数的最大值，而不是各预测阶段应成本项的上界之和，从而减少了与最小－最大优化相关的方程个数，有利于降低计算复杂性，文中进一步证明了不确定系统的闭环稳定性取决于优化问题在初始时刻的可行性，并将优化问题转化为线性矩阵不等式形式。最后，以数值仿赵例子验证了方法的有效性。     

Simultaneous Pseudo-Timestepping for PDE-Model Based Optimization Problems

In this paper we present a new method for the solution of optimization problems with PDE constraints. It is based on simultaneous pseudo-time stepping for evolution equations. The new method can be viewed as a continuous reduced SQP method in the sense that it uses a preconditioner derived from that method. The reduced Hessian in the preconditioner is approximated by a pseudo-differential operator, whose symbol can be investigated analytically. We apply our method to a boundary control model problem. The new optimization method needs 3.2-times the overall computational effort of the solution of simulation problem alone.     

胶束薄层色谱法测定食品及药用胶囊中的合成食用色素

在聚酰胶片上以13%SDBS(十二烷基苯磺酸钠)+7%Triton X-100〔聚乙二醇-对-(1.1,3,3-四甲基)丁基苯基醚〕+(2+3)氨水(1+5+1)为展开剂,用胶束薄层色谱法分离和测定了柠檬黄、亮蓝、苋菜红、胭脂红和赤藓红。测定波长分别为440、632、535、525和528 nm,线性范围分别为0.1～2、0.05～1、0.05～1、0.05～1和0.05～1μg/斑点。方法用于测定软饮料糖果及药用胶囊中的合成色素,获得了满意的结果。此外,还对展开剂的最佳组成进行了单纯形法优化。     

A class of polynomial variable metric algorithms for linear optimization

In the paper, the behaviour of interior point algorithms is analyzed by using a variable metric method approach. A class of polynomial variable metric algorithms is given achieving O ((n/β)L) iterations for solving a canonical form linear optimization problem with respect to a wide class of Riemannian metrics, wheren is the number of dimensions and β a fixed value. It is shown that the vector fields of several interior point algorithms for linear optimization is the negative Riemannian gradient vector field of a linear a potential or a logarithmic barrier function for suitable Riemannian metrics. Research Partially supported by the Hungarian National Research Foundation, Grant Nos. OTKA-T016413 and OTKA-2116.