An Algorithmic Approach to Constructing Supersaturated Designs

Nguyen, Nam-Ky

doi:10.1080/00401706.1996.10484417

Cited by 134 publications

(94 citation statements)

References 12 publications

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“…Thus, sij is a measure of the nonorthogonality between factors i and j . A lower bound for E(s2)-optimal designs provided by Nguyen (1996) and Tang and Wu (1997) is…”

Section: Supersaturated Designsmentioning

confidence: 99%

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Supersaturated Latin Hypercube Designs

Butler

2005

Communications in Statistics - Theory and Methods

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Latin hypercube designs have received much recent interest in the context of computer experiments where there may be many input variables (or factors). Supersaturated designs offer an eficient method of determining the factors that have the most substantial effect on the response of interest. In this article, supersaturated Latin hypercube designs are constructed and they are shown to have a number of favorable properties including near-E(s2)-optimality. They are also shown to facilitate a straighrforward analysis in terms of the spectral properties of the designs.

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“…Thus, sij is a measure of the nonorthogonality between factors i and j . A lower bound for E(s2)-optimal designs provided by Nguyen (1996) and Tang and Wu (1997) is…”

Section: Supersaturated Designsmentioning

confidence: 99%

“…These designs are particularly useful when there are very many factors, when experimentation is relatively expensive, and when the assumption of effect sparsity is not unreasonable. Supersaturated designs have also mainly been considered when the factors are restricted to two levels; see Nguyen (1996), Tang and Wu (1997), and Butler et al…”

Section: Introductionmentioning

confidence: 99%

Supersaturated Latin Hypercube Designs

Butler

2005

Communications in Statistics - Theory and Methods

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“…Both Nguyen (1996) and Tang and Wu (1997) independently derived a useful lower bound for the design criterion of a balanced SSD with m -factors and N -runs:

E false(s^{2} false) \geq \frac{m - N + 1}{false(m - 1 false) false(N - 1 false)} N^{2} .

…”

Section: Introductionmentioning

confidence: 99%

Optimizing Two-Level Supersaturated Designs Using Swarm Intelligence Techniques

et al. 2016

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Supersaturated designs (SSDs) are often used to reduce the number of experimental runs in screening experiments with a large number of factors. As more factors are used in the study, the search for an optimal SSD becomes increasingly challenging because of the large number of feasible selection of factor level settings. This paper tackles this discrete optimization problem via an algorithm based on swarm intelligence. Using the commonly used E(s2) criterion as an illustrative example, we propose an algorithm to find E(s2)–optimal SSDs by showing that they attain the theoretical lower bounds in Bulutoglu and Cheng (2004) and Bulutoglu (2007). We show that our algorithm consistently produces SSDs that are at least as efficient as those from the traditional CP exchange method in terms of computational effort, frequency of finding the E(s2)-optimal SSD and also has good potential for finding D3–, D4– and D5–optimal SSDs.

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“…Various ®elds of research may bene®t from the use of supersaturated designs, including computer and medical experiments (Lin, 1995), industrial and engineering experiments (Wu, 1993;Nguyen, 1996) and biometric applications (Eskridge et al, 2000).…”

Section: Introductionmentioning

confidence: 99%

“…The ®rst systematic designs were provided by Booth and Cox (1962) who used the maxjsj and Es 2 -optimality criteria for constructing and comparing designs. In recent times, the Es 2 -optimality criterion has been the main criterion used; see Lin (1993Lin ( , 1995, Wu (1993), Nguyen (1996), Tang and Wu (1997), Cheng (1997) and Eskridge et al (2000).…”

Section: Introductionmentioning

confidence: 99%

A General Method of Constructing E(s2)-Optimal Supersaturated Designs

Butler

Mead

Eskridge

et al. 2001

Journal of the Royal Statistical Society Series B: Statistical Methodology

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There has been much recent interest in supersaturated designs and their application in factor screening experiments. Supersaturated designs have mainly been constructed by using the E s 2 -optimality criterion originally proposed by Booth and Cox in 1962. However, until now E (s 2 )optimal designs have only been established with certainty for n experimental runs when the number of factors m is a multiple of n À 1, and in adjacent cases where m q(n À 1) r (jr j 4 2, q an integer). A method of constructing E (s 2 )-optimal designs is presented which allows a reasonably complete solution to be found for various numbers of runs n including n 8, 12, 16, 20, 24, 32, 40, 48, 64.

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An Algorithmic Approach to Constructing Supersaturated Designs

Cited by 134 publications

References 12 publications

Supersaturated Latin Hypercube Designs

Supersaturated Latin Hypercube Designs

Optimizing Two-Level Supersaturated Designs Using Swarm Intelligence Techniques

A General Method of Constructing E(s2)-Optimal Supersaturated Designs

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