Communications for Statistical Applications and Methods

The Korean Statistical Society (한국통계학회)
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Improved Two Points Algorithm For D-optimal Design

  • Published : 1999.04.01
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Abstract

To improve the slow convergence property of the steepest ascent type algorithm for continuous D-optimal design problems. we develop a new algorithm. We apply the nonlinear system of equations as the necessary condition of optimality and develop the two-point algorithm that solves the problem of clustering. Because of the nature of the steepest coordinate ascent algorithm avoiding the problem of clustering itself helps the improvement of convergence speed. The numerical examples show the performances of the new method is better than those of various steepest ascent algorithms.

Keywords

References

  1. The Annals of Statistics v.1 no.2 Sequences Converging to D-optimal Designs of Experiments Atwood, C.L.
  2. Department of Numerical Analysis and Computing Science Studies in D-Optimal Experimental Design Betsis, D.
  3. A Steepest Descent Clustering Algorithm for Determinant-Maximizing Regression Experimental Designs Edahl,R.;Kortanek, K.O.
  4. Theory of Optimal Experiments Fedorov, V.V.
  5. Canadian Journal of Mathematics v.12 The equivalence of two Extremum problems Kiefer. J.;Wolfowitz. J.
  6. Linear Optimal Experimental Designs Tsay, J.Y.
  7. Annals of Mathematical Statistics v.41 The Sequential Generation of D-Optimum Experimental Designs Wynn, H.P.