An Algorithm for the Construction of “D-Optimal” Experimental Designs

By T. Mitchell. Technometrics, 2000

477 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Industrial and Manufacturing Engineering, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/cb43eabd34bb577e79578bbdfea22804560aa593

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In Computational Theory and Mathematics

In Industrial and Manufacturing Engineering

In Management Science and Operations Research

Citations in the corpus

Outperforming Completely Randomized Designs (2004, methodology): “The most famous construction algorithms for response surface designs may be classified as point exchange algorithms and include the algorithm of Fedorov (1972), the DETMAX algorithm of Mitchell (1974) and the BLKL al gorithm of Atkinson and Donev (1992).”
Design and Analysis of Experiments (2001, unknown): “As mentioned earlier in this section, this can lead to situations where the model that is considered is much more complex than the first- and second-order polynomials typically employed in response surface methodology [see, for example, Barton (1992, 1994), Mitchell and Morris (1992), and Simpson an”
Least squares polynomial chaos expansion: A review of sampling strategies (2017, methodology): “Other popular exchange algorithms include DETMAX algorithm [82], Wynn’s algorithm [124], k -exchange algorithm [57], kl -exchange algorithm [4], and coordinate exchange algo-rithm of [78] which does not require a candidate set.”
A deterministic algorithm for experimental design applied to tomographic and microseismic monitoring surveys (2004, methodology): “…are gradually modified into improved designs (e.g. Kijko 1977; Rabinowitz & Steinberg 1990; Steinberg et al. 1995, all using the heuristic method of Mitchell 1974), or use stochastic optimization methods (e.g. genetic algorithm or simulated annealing) to find sets of good designs (e.g. Smith et…”
Stochastic natural frequency of composite conical shells (2015, methodology): “To mitigate this lacuna, D-optimal design technique [1–5] is employed in the present analysis to map the inadvertent uncertainties efficiently.”
Review of Metamodeling Techniques in Support of Engineering Design Optimization (2007, methodology): “Widely used “classic” experimental designs include factorial or fractional factorial [8], central composite design (CCD) [8, 9], Box-Behnken [8], alphabetical optimal [10, 11], and Plackett-Burman designs [8].”