On the Isomorphism of Fractional Factorial Designs

Two fractional factorial designs are called isomorphic if one can be obtained from the other by relabeling the factors, reordering the runs, and switching the levels of factors. To identify the isomorphism of two s-factor n-run designs is known to be an NP hard problem, when n and s increase. There is no tractable algorithm for the identification of isomorphic designs. In this paper, we propose a new algorithm based on the centered L₂-discrepancy, a measure of uniformity, for detecting the isomorphism of fractional factorial designs. It is shown that the new algorithm is highly reliable and can significantly reduce the complexity of the computation. Theoretical justification for such an algorithm is also provided. The efficiency of the new algorithm is demonstrated by using several examples that have previously been discussed by many others.     

Design isomorphisms and group isomorphisms

In this paper conditions are given which imply that a design isomorphism between designs with group actions is in fact a group isomorphism. The conditions are geometric. Automorphism groups are then calculated for a family of designs using the geometric conditions.The research in the paper was conducted at Loyola University of Chicago.     

一种构造三水平因子超饱和设计的准则和算法

在工业统计试验和其他科学试验中,常遇到因子个数多而所允许的试验次数少的情况,这时要用到超饱和设计,以前的文章仅研究了二水平因子的超饱和设计,本文对于三水平因子的超饱和设计提出了一种基于典则相关意义下的优良性准则和构造算法,并给出了试验次数为９和１８可分别安排到２６和３０个因子的三水平超饱和设计表。     

一类新的１６次试验的超饱和设计

该文利用Ｈａｌｌ（１９６１）提出的１６阶的Ｈａｄａｍａｒｄ阵之一构造了一类新的１６次试验的二水平因子超饱和设计，讨论了这类新的设计的统计性质并与其他已有类似设计作了比较，同时指出了该类新设计的适用范围．     

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.     

Concerning the complexity of deciding isomorphism of block designs

A construction is described to encode an arbitrary graph uniquely as a block design. This demonstrates that describing whether two block designs (without repeated blocks) are isomorphic is polynomial time equivalent to solving graph isomorphism. This result supplies evidence for the claim that isomorphism testing for block designs is a hard subcase of graph isomorphism.     

An efficient method for constructing uniform designs with large size

Many construction methods for (nearly) uniform designs have been proposed under the centered $L_2$ -discrepancy, and most of them are only suitable for constructing designs with small size. This paper proposes a new method, called mixture method (MM), to construct nearly symmetrical/asymmetrical uniform designs with large number of runs and/or large number of factors. The new method has the “better than given” property, i.e., the resulting design is better than existing designs in the sense of the pre-decided criterion. Moreover, the computational speed of MM is faster than most existing methods.     

Geometric equivalence of groups

Based on the notion of geometric equivalence of groups, new classes of groups, namely, geometric varieties of groups, are defined. Some properties of such classes, including their relation to quasi-varieties and prevarieties of groups, are studied. Examples of torsion free nilpotent groups that are geometrically nonequivalent to their minimal completions, as well as an example of centrally metabelian groups that are geometrically nonequivalent but generate equal quasi-varieties, are given.     

Trading Signed Designs and Some New 4-(12, 5, 4) Designs

Variations of the trade-off method exist in the literature of design theory and have been utilized by some authors to produce some t-designs with or without repeated blocks. In this paper we explore a new version of this algorithmic method (i) to produce 20 nonisomorphic and rigid 4-(12,5,4) designs, (ii) to study the spectrum of support sizes of 4-(12,5,4) designs. Along these, we also present a new design invariant for testing isomorphism among designs and a new way of representing t-designs.     

Generating and improving orthogonal designs by using mixed integer programming

Analysts faced with conducting experiments involving quantitative factors have a variety of potential designs in their portfolio. However, in many experimental settings involving discrete-valued factors (particularly if the factors do not all have the same number of levels), none of these designs are suitable.In this paper, we present a mixed integer programming (MIP) method that is suitable for constructing orthogonal designs, or improving existing orthogonal arrays, for experiments involving quantitative factors with limited numbers of levels of interest. Our formulation makes use of a novel linearization of the correlation calculation.The orthogonal designs we construct do not satisfy the definition of an orthogonal array, so we do not advocate their use for qualitative factors. However, they do allow analysts to study, without sacrificing balance or orthogonality, a greater number of quantitative factors than it is possible to do with orthogonal arrays which have the same number of runs.     

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.     

CONSTRUCTING UNIFORM DESIGNS WITH TWO- OR THREE-LEVEL

When the number of runs is large, to search for uniform designs in the sense of low-discrepancy is an NP hard problem. The number of runs of most of the available uniform designs is small (≤50). In this article, the authors employ a kind of the so-called Hamming distance method to construct uniform designs with two- or three-level such that some resulting uniform designs have a large number of runs. Several infinite classes for the existence of uniform designs with the same Hamming distances between any distinct rows are also obtained simultaneously. Two measures of uniformity, the centered L2-discrepancy (CD, for short) and wrap-around L2-discrepancy (WD, for short), are employed.     

Dimension matrix of the G-M fractal

Fractals which represent many of the sets in various scientific fields as well as in nature is geometrically too complicate. Then we usually use Hausdorff dimension to estimate their geometrical properties. But to explain the fractals from the Hausdorff dimension induced by the Euclidan metric are not too sufficient. For example, in digital communication, while encoding or decoding the fractal images, we must consider not only their geometric sizes but also many other factors such as colours, densities and energies etc.. So in this paper we define the dimension matrix of the sets by redefining the new metric.     

Run orders and quantitative factors in asymmetrical designs

Results on run orders leading to trend-free symmetrical factorial designs are extended to the asymmetrical case, using the character theory of abelian groups. The tools developed apply equally to the construction of designs for quantitative treatment factors with eight or more regularly spaced levels. Abelian group theory can also be used to find minimum-cost run orders for asymmetrical designs, with a cost based on the number of changes of levels between successive runs.     

Some Hermite–Hadamard type inequalities for geometrically quasi-convex functions

In the paper, we introduce a new concept ‘geometrically quasi-convex function’ and establish some Hermite–Hadamard type inequalities for functions whose derivatives are of geometric quasi-convexity.     

A Note on Point Stabilizers in Sharp Permutation Groups of Type {0, k}

We study sharp permutation groups of type {0, k} and observe that, once the isomorphism type of a point stabilizer is fixed, there are only finitely many possibilities for such a permutation group. We then show that a sharp permutation group of type {0, k} in which a point stabilizer is isomorphic to the alternating group on 5 letters must be a geometric group. There is, up to permutation isomorphism, one such permutation group.     

The logarithmic least squares and the generalized pseudoinverse in estimating ratios

This paper concerns the pairwise-comparison method used in Analytic Hierarchy Process (AHP). The logarithmic least square method is one of the methods used to rank a finite number of stimuli based on their pairwise-comparison. In the case of one decision-maker the problem can be solved using the geometric mean method. It is then assumed that the solution is geometrically normalized. In the case of multiple decision makers a set of linear equations is obtained and if we have a different number of judgments for each pair of the compared objects the geometric normalization assumption can not be used directly. The aim of this paper is to show that applying the generalized pseudoinverse we obtain the solution that is geometrically normalized and consistent with the case of one decision maker. To define the pseudoinverse the spectral decomposition is used. The structure of the general solution is presented and the existence of the general solution is discussed.     

Adding more observations to saturated D‐optimal resolution III two‐level factorial designs

We consider the class of saturated main effect plans for the 2^k factorial. With these saturated designs, the overall mean and all main effects can be unbiasedly estimated provided that there are no interactions. However, there is no way to estimate the error variance with such designs. Because of this and other reasons, we like to add some additional runs to the set of (k+1) runs in the D‐optimal design in this class. Our goals here are: (1) to search for s additional runs so that the resulting design based on (k+s+1) runs yields a D‐optimal design in the class of augmented designs; (2) to classify all the runs into equivalent classes so that the runs in the same equivalent class give us the same value of the determinant of the information matrix. This allows us to trade runs for runs if this becomes necessary; (3) to obtain upper bounds for determinant of the information matrices of augmented designs. In this article we shall address these approaches and present some new results. © 2002 Wiley Periodicals, Inc. J Combin Designs 11: 51–77, 2003; Published online in Wiley InterScience ( www.interscience.wiley.com ). DOI 10.1002/jcd.10026     

Limits of limit sets I

We show that for a strongly convergent sequence of geometrically finite Kleinian groups with geometrically finite limit, the Cannon–Thurston maps of limit sets converge uniformly. If however the algebraic and geometric limits differ, as in the well known examples due to Kerckhoff and Thurston, then provided the geometric limit is geometrically finite, the maps on limit sets converge pointwise but not uniformly.     

Microlocal Analysis of the Geometric Separation Problem

Image data are often composed of two or more geometrically distinct constituents; in galaxy catalogs, for instance, one sees a mixture of pointlike structures (galaxy superclusters) and curvelike structures (filaments). It would be ideal to process a single image and extract two geometrically “pure” images, each one containing features from only one of the two geometric constituents. This seems to be a seriously underdetermined problem but recent empirical work achieved highly persuasive separations. We present a theoretical analysis showing that accurate geometric separation of point and curve singularities can be achieved by minimizing the ?₁ norm of the representing coefficients in two geometrically complementary frames: wavelets and curvelets. Driving our analysis is a specific property of the ideal (but unachievable) representation where each content type is expanded in the frame best adapted to it. This ideal representation has the property that important coefficients are clustered geometrically in phase space, and that at fine scales, there is very little coherence between a cluster of elements in one frame expansion and individual elements in the complementary frame. We formally introduce notions of cluster coherence and clustered sparsity and use this machinery to show that the underdetermined systems of linear equations can be stably solved by ?₁ minimization; microlocal phase space helps organize the calculations that cluster coherence requires. © 2012 Wiley Periodicals, Inc.