Determinant Maximization with Linear Matrix Inequality Constraints
SIAM Journal on Matrix Analysis and Applications
Two complexity results on c-optimality in experimental design
Computational Optimization and Applications
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An experimental design is said to be c-optimal if it minimizes the variance of the best linear unbiased estimator of c^T@b, where c is a given vector of coefficients, and @b is an unknown vector parameter of the model in consideration. For a linear regression model with uncorrelated observations and a finite experimental domain, the problem of approximate c-optimality is equivalent to a specific linear programming problem. The most important consequence of the linear programming characterization is that it is possible to base the calculation of c-optimal designs on well-understood computational methods. In particular, the simplex algorithm of linear programming applied to the problem of c-optimality reduces to an exchange algorithm with different pivot rules corresponding to specific techniques of selecting design points for exchange. The algorithm can also be applied to ''difficult'' problems with singular c-optimal designs and relatively high dimension of @b. Moreover, the algorithm facilitates identification of the set of all the points that can support some c-optimal design. As an example, optimal designs for estimating the individual parameters of the trigonometric regression on a partial circle are computed.