计算机实验的正交空间填充设计

探讨了一类正交的空间填充设计,它们是在正交约束下优化某种空间填充性准则的优化问题的解.文章提出了一个分组坐标下降算法来求解该优化问题.数值模拟表明这个算法能够有效地找到具有很好空间填充性质的正交设计.另外,正交的最大最小距离设计在计算机实验预测方面的表现与常用的设计是可比的.     

嵌套拉丁超立方设计的优化

由于嵌套拉丁超立方设计(nested Latin hypercube design,NLHD)在整个设计区域中并不总具有很好的空间填充性,文章提出了一个分层加强随机进化算法对NLHD进行优化,提高它的空间填充性.该算法优先考虑在实际中更重要的底层设计,逐层优化后得到最终的NLHD.提出了三类基本操作来搜索更好的设计,并保证了每步更新后的设计依然保持NLHD的结构.算例表明该算法速度快,效率高.     

正交空间填充设计的最新结果

正交性与空间填充性是计算机试验设计的两个重要性质.本文提出通用的旋转方法用以构造一类新的正交空间填充设计.这类设计既具有正交性,又有理想的空间填充性.此外,本文提出的构造方法简单易行,且生成的设计具有灵活的试验次数和水平数.生成的设计既可以是对称的,也可以是非对称的.相关理论支撑科学严谨.本文构造并给出许多新的具有理想的空间填充性的正交设计.旋转矩阵和差阵在构造中起到重要的作用.     

投影均匀分片拉丁超立方体设计

空间填充设计是有效的计算机试验设计,比如均匀设计、最大最小距离拉丁超立方体设计等.虽然这些设计在整个试验空间中有较好的均匀性,但其低维投影均匀性可能并不理想.对于因子是定量的计算机试验,已有文献构造了诸如最大投影设计、均匀投影设计等相适应的设计;而对于同时含有定性因子和定量因子的计算机试验,尚未有投影均匀设计的相关文献.文章提出了综合投影均匀准则,利用门限接受算法构造了投影均匀的分片拉丁超立方体设计.在新构造设计中,整体设计与每一片设计均具有良好的投影均匀性.模拟结果显示,与随机分片拉丁超立方体设计相比,利用新构造设计进行试验而拟合的高斯过程模型具有更小的均方根预测误差.     

混料试验的拟分量变换设计

在混料试验中有两类重要的设计,分别是最优设计与均匀设计.为了构造一类能兼顾最优性与均匀性的混料设计,本文在Scheffe型设计的基础上提出了混料拟分量变换设计.首先定义了填充格子点集并讨论了它的相关性质,然后以填充格子点集作为NT-net用于度量点集的均匀程度,证明了对称区域内的中心压缩拟分量变换设计最均匀,以及D-,A-最优设计的拟分量变换设计具有最优性不变的性质.通过实例分析,验证了拟分过变换设计的有效性,并且这种设计能保证在变换区域内的最优性以及整体区域的均匀性.这种方法所建立的模型与真实模型更接近,更稳健.     

低秩张量填充的加速随机临近梯度算法

本文提出了一种求解低秩张量填充问题的加速随机临近梯度算法.张量填充模型可以松弛为平均组合形式的无约束优化问题,在迭代过程中,随机选取该组合中的某一函数进行变量更新,有效减少了张量展开、矩阵折叠及奇异值分解带来的较大的计算花费.本文证明了算法的收敛率为$O (1/k^{2})$.最后,随机生成的和真实的张量填充实验结果表明新算法在CPU时间上优于现有的三种算法.     

一类正交空间填充设计的构造

空间填充设计在计算机试验中应用十分广泛,当拟合回归模型时,正交的空间填充设计保证了因子效应估计的独立性.基于广义正交设计,文章给出了构造二阶正交拉丁超立方体设计和列正交设计的方法,新构造的设计不仅满足任意两列之间相互正交,还能保证每一列与任一列元素平方组成的列以及任两列元素相乘组成的列都正交.当某些正交的空间填充设计不存在时,具有较小相关系数的近似正交设计可作为替代设计使用.设计构造的灵活性为计算机试验在实践中的广泛应用提供了必要的支持.     

大型二四混水平均匀设计的构造及其性质

Doubling、tripling和quadrupling方法可用于构造大型的具有优良性质的二水平、三水平和四水平的空间填充设计.均匀设计作为一种空间填充设计被应用到许多领域.Li,et al.(2021)结合doubling和tripling方法构造大型二三混水平均匀设计.文章结合doubling和quadrupli...     

基于特殊混料模型D-最优设计搜索的交换点式门限接受算法

在搜索混料模型D-最优设计的计算机算法领域,主流算法包括经典的Fedorov算法,以及元启发类算法,但两者在一些特定的优化问题上,分别在收敛速度和收敛精度方面有进一步提升的空间.文章分别探讨了可能造成这种情况的两类算法各自的局限性,并采取优势互补的策略,构建了交换点式门限接受算法,即ETA (exchange threshold accepting)算法.以含倒数项混料模型为例,文章验证了ETA算法生成设计的D-最优性,并分别与Fedorov算法和元启发类的ProjPSO算法作比较.结果表明,至少在某些特殊的混料模型D-最优设计的搜索方面,ETA算法在收敛速度和精度方面均具有一定的优势.     

含定性因子的二阶Scheff′e模型多目标最优设计北大核心

在实际的很多情形中,混料试验都具有多个目标,且响应变量不仅受到各分量的影响,还会受到其他定性因子变量的影响.文中基于一类含定性因子的混料模型,通过求解效率和极值得到多目标最优设计,并证明了该设计满足相应组合最优性.实例证明,该方法同样适用于3个或更多目标优化设计问题.     

Construction of optimal supersaturated designs by the packing method

A supersaturated design is essentially a factorial design with the equal occurrence of levels property and no fully aliased factors in which the number of main effects is greater than the number of runs. It has received much recent interest because of its potential in factor screening experiments. A packing design is an important object in combinatorial design theory. In this paper, a strong link between the two apparently unrelated kinds of designs is shown. Several criteria for comparing supersaturated designs are proposed, their properties and connections with other existing criteria are discussed. A combinatorial approach, called the packing method, for constructing optimal supersaturated designs is presented, and properties of the resulting designs are also investigated. Comparisons between the new designs and other existing designs are given, which show that our construction method and the newly constructed designs have good properties.     

一种构造多水平超饱和设计的新方法

超饱和设计在搜索试验中有重要应用.自上世纪九十年代迄今, 超饱和设计的研究取得了丰硕成果,研究的设计从二水平发展到多水平, 进而到混合水平;构造方法从巧妙的构思到利用组合理论进行系统构造,进而到各种算法的开发; 对于超饱和设计的评优准则也有更深入的认识.本文介绍一种构造多水平超饱和设计的新方法. 这种方法简单易行,很容易构造出具有优良性质的多水平超饱和设计, 有很强的实用性.     

T‐optimality and neural networks: a comparison of approaches for building experimental designs

This paper deals with optimal experimental design criteria and neural networks in the aim of building experimental designs from observational data. It addresses the following three main issues: (i) the introduction of two radically different approaches, namely T‐optimal designs extended to Generalized Linear Models and Evolutionary Neural Networks Design; (ii) the proposal of two algorithms, based on model selection procedures, to exploit the information of already collected data; and (iii) the comparison of the suggested methods and corresponding algorithms by means of a simulated case study in the technological field. Results are compared by considering elements of the proposed algorithms, in terms of models and experimental design strategies. In particular, we highlight the algorithmic features, the performances of the approaches, the optimal solutions and the optimal levels of variables involved in a simulated foaming process. The optimal solutions obtained by the two proposed algorithms are very similar, nevertheless, the differences between the paths followed by the two algorithms to reach optimal values are substantial, as detailed step‐by‐step in the discussion. Copyright © 2012 John Wiley & Sons, Ltd.     

混水平均匀设计的构造

我们用离散偏差来度量部分因子设计的均匀性,本文的目的在于寻找一些构造混水平均匀设计的方法,这些方法比文献中已有的方法更简单且计算成本更低.我们得到了离散偏差的一个下界,如果一个U 型设计的离散偏差值达到这个下界,那么该设计是—个均匀设计.我们建立了均匀设计与组合设计理论中一致可分解设计之间的联系.通过一致可分解设计,我们提出了一些构造均匀设计的新方法,同时也给出了许多均匀设计存在的无穷类.     

The usefulness of Bayesian optimal designs for discrete choice experiments

Recently, the use of Bayesian optimal designs for discrete choice experiments, also called stated choice experiments or conjoint choice experiments, has gained much attention, stimulating the development of Bayesian choice design algorithms. Characteristic for the Bayesian design strategy is that it incorporates the available information about people's preferences for various product attributes in the choice design. This is in contrast with the linear design methodology, which is also used in discrete choice design and which depends for any claims of optimality on the unrealistic assumption that people have no preference for any of the attribute levels. Although linear design principles have often been used to construct discrete choice experiments, we show using an extensive case study that the resulting utility‐neutral optimal designs are not competitive with Bayesian optimal designs for estimation purposes. Copyright © 2011 John Wiley & Sons, Ltd.     

A robustness approach to uncapacitated network design problems

In this paper, we address uncapacitated network design problems characterised by uncertainty in the input data. Network design choices have a determinant impact on the effectiveness of the system. Design decisions are frequently made with a great degree of uncertainty about the conditions under which the system will be required to operate. Instead of finding optimal designs for a given future scenario, designers often search for network configurations that are “good” for a variety of likely future scenarios. This approach is referred to as the “robustness” approach to system design. We present a formal definition of “robustness” for the uncapacitated network design problem, and develop algorithms aimed at finding robust network designs. These algorithms are adaptations of the Benders decomposition methodology that are tailored so they can efficiently identify robust network designs. We tested the proposed algorithms on a set of randomly generated problems. Our computational experiments showed two important properties. First, robust solutions are abundant in uncapacitated network design problems, and second, the proposed algorithms performance is satisfactory in terms of cost and number of robust network designs obtained.     

Efficient space-filling and non-collapsing sequential design strategies for simulation-based modeling

Simulated computer experiments have become a viable cost-effective alternative for controlled real-life experiments. However, the simulation of complex systems with multiple input and output parameters can be a very time-consuming process. Many of these high-fidelity simulators need minutes, hours or even days to perform one simulation. The goal of global surrogate modeling is to create an approximation model that mimics the original simulator, based on a limited number of expensive simulations, but can be evaluated much faster. The set of simulations performed to create this model is called the experimental design. Traditionally, one-shot designs such as the Latin hypercube and factorial design are used, and all simulations are performed before the first model is built. In order to reduce the number of simulations needed to achieve the desired accuracy, sequential design methods can be employed. These methods generate the samples for the experimental design one by one, without knowing the total number of samples in advance. In this paper, the authors perform an extensive study of new and state-of-the-art space-filling sequential design methods. It is shown that the new sequential methods proposed in this paper produce results comparable to the best one-shot experimental designs available right now.     

Heuristics and their design: a survey

The purpose of this paper is a design oriented survey of heuristics. Since the main application fields of heuristics are problems of the combinatorial type, an introductory synopsis of combinatorial problems is first presented (Section 2). Heuristics are a specific kind of algorithms; therefore, the position of heuristics within the system of algorithms is described (Section 3). The design of heuristics requires decisions, and decisions are choices among alternatives which have to be explicitly available; a basis for this is presented in a morphological classification of heuristics (Section 4). Based on the classification, some aspects of the design process will be considered (Section 5).     

混料试验的D—最优对称设计,算法及应用

本文着重研究了混料试验的D—最优对称设计.基于Fedorov及Atwood的迭代方法,作者给出一个构造D—最优对称设计的改进算法.这个新算法由双循环迭代构成:从初始设计中减去最小方差对称点的设计测度;增加设计测度于最大方差的对称设计点,同时,本算法还只在对称子区域中寻找最大方差设计点,这样就使得Fedorov算法的收敛速度有了显著地提高,并能构造出更高效的D—最优对称设计.另外还给出一些构造实例.