The method used to find an optimal design is similar to the coordinate exchange algorithm described in Meyer and Nachtsheim (1995). For more information about how the nonlinear optimal design is obtained, see Gotwalt et al. (2009). The general approach proceeds as follows:
• Random designs are tested until a nonsingular starting design is found.
• Iterations are conducted, where each iteration consists of a pass through all the runs.
• For each run, factors are optimized one at a time.
• The objective function is the Bayesian D-optimality criterion. This is the expectation of the logarithm of the determinant of the information matrix with respect to the prior distribution.
• Iterations terminate once the change in the objective function is small.