直接由SiO2低温合成含硅环氧化合物及其结构表征

研究了直接由SiO2(沉淀白炭黑)、乙二醇、KOH为原料低温合成高活性的五配位有机硅络合物[KSi-(OCH2CH2O)2OCH2CH2OH]然后与环氧氯丙烷反应,生成含硅环氧化合物,并借助于红外、核磁共振、热分析、能谱元素分析等现代测试手段,对合成的产物进行了结构表征。     

应用分割矩阵全局优化算法设计用于编码孔径成像的二维阵列

二维编码阵列是编码孔径成像的关键部件,它直接决定着再现的层析图像的质量。目前仍没有一种理想的二维阵列既具有较高的量子收集率,又具有良好的层析成像特性。采用一种新的方法———分割矩阵(DIRECT)全局优化算法,设计二维阵列,该算法适用于多变量“黑盒”问题的求解,并且具有比其他优化算法更快的收敛速度。其目的是设计一类自相关函数旁瓣最大值为1,同时具有最大填充率的二维编码阵列。理论分析及实验结果表明:用该算法搜索得到的二维阵列既具有较高的量子收集率,又具有良好的层析成像特性。     

Designing PMF with negative tap coefficient

DIRECT global optimization algorithm is innovatively used in design of PMF. Tap coefficient is attained by achieving DIRECT algorithm with C language and Matlab language is employed to simulate the amplitude response of filters. We have designed PMF (photonic microwave filter) with negative tap coefficient and then optimize the design. The minimum shape factor is as low as 1.3180. The results show that the algorithm is highly efficient in designing filter and can produce necessary passband and stopband response characteristics. In the meantime, transition band becomes steeper in the process of further optimization and the shape factor is closer to 1.     

Discrete Approximation of a Mixture Distribution via Restricted Divergence

Mixture distributions arise in many application areas, for example, as marginal distributions or convolutions of distributions. We present a method of constructing an easily tractable discrete mixture distribution as an approximation to a mixture distribution with a large to infinite number, discrete or continuous, of components. The proposed DIRECT (divergence restricting conditional tesselation) algorithm is set up such that a prespecified precision, defined in terms of Kullback–Leibler divergence between true distribution and approximation, is guaranteed. Application of the algorithm is demonstrated in two examples. Supplementary materials for this article are available online.     

Design of URAs by DIRECT global optimization algorithm

Coded aperture imaging (CAI) has evolved as a standard technique for imaging high-energy photon sources and has found numerous applications. Coded aperture arrays (CAAs) are the most important devices in the applications of CAI. In recent years, many approaches were presented to design optimum or near-optimum CAAs. Uniformly redundant arrays (URAs) are the most successful CAAs for their cyclic autocorrelation consisting of a sequence of delta functions on a flat sidelobe, which can easily be subtracted when the object has been reconstructed. Unfortunately, the existing methods can only be used to design URAs with limited number of array sizes and fixed autocorrelative sidelobe-to-peak ratio. In this paper, we presented a method to design more flexible URAs by means of a global optimization algorithm named DIRECT. By our approaches, we obtain various types of URAs including the filled URAs which can be constructed by existing methods and the sparse URAs which have never been constructed and mentioned by existing papers as far as we know.     

Design and implementation of a massively parallel version of DIRECT

This paper describes several massively parallel implementations for a global search algorithm DIRECT. Two parallel schemes take different approaches to address DIRECT’s design challenges imposed by memory requirements and data dependency. Three design aspects in topology, data structures, and task allocation are compared in detail. The goal is to analytically investigate the strengths and weaknesses of these parallel schemes, identify several key sources of inefficiency, and experimentally evaluate a number of improvements in the latest parallel DIRECT implementation. The performance studies demonstrate improved data structure efficiency and load balancing on a 2200 processor cluster.     

Additive Scaling and the DIRECT Algorithm

In this paper we show that the convergence behavior of the DIviding RECTangles (DIRECT) algorithm is sensitive to additive scaling of the objective function. We illustrate this problem with a computation and show how the algorithm can be modified to eliminate this sensitivity.     

Dynamic Data Structures for a Direct Search Algorithm

The DIRECT (DIviding RECTangles) algorithm of Jones, Perttunen, and Stuckman (Journal of Optimization Theory and Applications, vol. 79, no. 1, pp. 157–181, 1993), a variant of Lipschitzian methods for bound constrained global optimization, has proved effective even in higher dimensions. However, the performance of a DIRECT implementation in real applications depends on the characteristics of the objective function, the problem dimension, and the desired solution accuracy. Implementations with static data structures often fail in practice, since it is difficult to predict memory resource requirements in advance. This is especially critical in multidisciplinary engineering design applications, where the DIRECT optimization is just one small component of a much larger computation, and any component failure aborts the entire design process. To make the DIRECT global optimization algorithm efficient and robust on large-scale, multidisciplinary engineering problems, a set of dynamic data structures is proposed here to balance the memory requirements with execution time, while simultaneously adapting to arbitrary problem size. The focus of this paper is on design issues of the dynamic data structures, and related memory management strategies. Numerical computing techniques and modifications of Jones' original DIRECT algorithm in terms of stopping rules and box selection rules are also explored. Performance studies are done for synthetic test problems with multiple local optima. Results for application to a site-specific system simulator for wireless communications systems (S ⁴ W) are also presented to demonstrate the effectiveness of the proposed dynamic data structures for an implementation of DIRECT.     

A Locally-Biased form of the DIRECT Algorithm

In this paper we propose a form of the DIRECT algorithm that is strongly biased toward local search. This form should do well for small problems with a single global minimizer and only a few local minimizers. We motivate our formulation with some results on how the original formulation of the DIRECT algorithm clusters its search near a global minimizer. We report on the performance of our algorithm on a suite of test problems and observe that the algorithm performs particularly well when termination is based on a budget of function evaluations.