Two Statistics and Machine Learning Toolbox™ algorithms generate D-optimal designs:
In practice, you may want to add runs to a completed experiment to learn more about a process and estimate additional model coefficients.
In many experimental settings, certain factors and their covariates are constrained to a fixed set of levels or combinations of levels.
Categorical factors take values in a discrete set of levels.
The row-exchange algorithm exchanges rows of an initial design matrix X with rows from a design matrix C evaluated at a candidate set of feasible treatments.