It is interesting that an obviously "wrong" approach can have more success than expected. However, most people these days would use more modern forms of regular

It is interesting that an obviously "wrong" approach can have more success than expected. However, most people these days would use more modern forms of regularization (e.g. ridge, Lasso, elastic net, stagewise with early stopping). Modern optimization software can handle it. As I noted in my other comment, equal-weight constraints are still used in one domain: neural network architectures (e.g. convolutional neural nets). This is not quite the same thing because neural nets are so highly parameterized, and the weights interact in so many nonlinear ways, that individual weights don't have any meaning independent of others. It does show us that the idea of equal-weight regression has still lived on in one specific place.