Computational Statistics & Data Analysis
Efficient computational algorithm for optimal allocation in regression models
Journal of Computational and Applied Mathematics
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We construct approximate optimizing distributions p"j by maximizing a criterion function subject to the basic constraints on p"j of nonnegativity and summation to unity. We use a class of multiplicative algorithms, indexed by a function f(.), which depends on the derivatives of the criterion function. The function may depend on one or more free parameters. To improve the convergence, we consider two approaches-Approach I and Approach II. Approach II is reported in Torsney and Mandal [2004. Multiplicative algorithms for constructing optimizing distributions: further developments. In: Bucchianico, A.D., Lauter, H., Wynn, H.P. (Ed.), MODA 7-Advances in Model-Oriented Design and Analysis. Physica, Heidelberg, pp. 163-171]. In Approach I, the criterion function can have only positive partial derivatives, whereas in Approach II the criterion can have both positive and negative derivatives. We consider objective choices of the class of functions f(.) for the two approaches. Considerable improvements in convergence are seen for each of the approaches.