Optimality Criterion Description Eq. #
\(J_{A}=trace\left(\left(\mathbf{\text{FIM}}\right)^{-1}\right)\) A-optimal design minimizes total parameter variance. (1.1)
\(J_{D}=det\left(\left(\mathbf{\text{FIM}}\right)^{-1}\right)\) D-optimal design minimizes the volume of the joint confidence interval for the parameters. (1.2)
\(J_{E}=\lambda_{\max}(\mathbf{\text{FIM}}^{-1})\) E-optimal design minimizes the largest eigenvalue of the FIM, thereby minimizing the uncertainties in the worst-case direction in the parameter space. (1.3)
\(J_{G}=max\ \left(\text{diag}\left(\mathbf{W}\left(\mathbf{\text{FIM}}\right)^{-1}\mathbf{W}^{\mathbf{T}}\right)\right)\) G-optimal design minimizes the maximum variance of model predictions at user-specified operating conditions of interest, specified using a matrix\(\ \mathbf{W}.\) This is equivalent to minimizing the largest value of the diagonal elements of \(\mathbf{W}\left(\mathbf{\text{FIM}}\right)^{-1}\mathbf{W}^{\mathbf{T}}\). (1.4)
\(J_{V}=trace\left(\mathbf{W}\left(\mathbf{\text{FIM}}\right)^{-1}\mathbf{W}^{\mathbf{T}}\right)\) V-optimal design minimizes the total variance of model predictions at user-specified operating conditions of interest, which are specified using matrix\(\ \mathbf{W}.\) This is equivalent to minimizing the trace of \(\mathbf{W}\left(\mathbf{\text{FIM}}\right)^{-1}\mathbf{W}^{\mathbf{T}}\). (1.5)