Non linear constraints in Cornerstone Design of Experiments

How can non-linear constraints be used in Cornerstone experimental designs?

The constraint editor of Cornerstone Experimental Design only allows for linear constraints: the involved factors may get a pre factor (coefficient) and the resulting weighted sum can be constrained by a certain limit. General constraints involving nonlinear functions like log(), exp(), sin(), tan() and even simpler ones like the product of 2 factors are not supported by the editor.


This usage example builds a workmap with an example that shows the path to construct the design obeying general constraints and that also respects them for optimization and prediction.

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