Do we need to use constrained optimization with Idzorek's method

Another reader has asked me whether they need to use constraints during the process of backing the uncertainty in a view out of a confidence when using Idzorek's method.

The thrust of their argument was that their view at 100% certainty was too extreme, and thus they would not consider holding the portfolio generated at 100% certainty.

Idzorek's method provides a simple and clean way to determine the variance of the view by backing it out from a confidence and the change in the weights based on an individual view. You don't need to use a constrained reverse optimization process, and in fact that would change the problem from a simple linear one to a much more complex one. If your views are too extreme, you can shrink the size of the return (Q vector) for that view, however what is important is the impact of the views at the confidence level you hold in them (what you do with the variance backed out from Idzorek's method) rather than the 100% confidence portfolio.

So even if you will use a constrained optimization with the final posterior returns, you do not need to use any type of constraints when computing the 100% confidence posterior as part of Idzorek's method. In fact if you use the formulas included in my paper, you don't really even see the 100% confidence portfolio.