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Multiplication Algorithms for Approximate Optimal Distributions with Cost Constraints

Author

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  • Lianyan Fu

    (School of Mathematics and Statistics, Liaoning University, Shenyang 110036, China
    These authors contributed equally to this work.)

  • Faming Ma

    (School of Economics, Liaoning University, Shenyang 110036, China
    These authors contributed equally to this work.)

  • Zhuoxi Yu

    (School of Mathematics and Statistics, Liaoning University, Shenyang 110036, China)

  • Zhichuan Zhu

    (School of Mathematics and Statistics, Liaoning University, Shenyang 110036, China)

Abstract

In this paper, we study the D - and A -optimal assignment problems for regression models with experimental cost constraints. To solve these two problems, we propose two multiplicative algorithms for obtaining optimal designs and establishing extended D -optimal ( E D -optimal) and A -optimal ( E A -optimal) criteria. In addition, we give proof of the convergence of the E D -optimal algorithm and draw conjectures about some properties of the E A -optimal algorithm. Compared with the classical D - and A -optimal algorithms, the E D - and E A -optimal algorithms consider not only the accuracy of parameter estimation, but also the experimental cost constraint. The proposed methods work well in the digital example.

Suggested Citation

  • Lianyan Fu & Faming Ma & Zhuoxi Yu & Zhichuan Zhu, 2023. "Multiplication Algorithms for Approximate Optimal Distributions with Cost Constraints," Mathematics, MDPI, vol. 11(8), pages 1-14, April.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:8:p:1963-:d:1129108
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    References listed on IDEAS

    as
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