[CWuestefeld]

I designed the ranking algorithm for our product search. There are several factors in it that are nonlinear.

For example, we obviously want the most-viewed and most-bought products to rise to the top. However, we also expect there's an initial honeymoon period for many new products, where people want to see them but they don't have enough history yet to sway the popularity factor. So there's a non-linear term that looks kind of like

  ( weight_factor / (product_age_days * age_scale_constant) )

[h0l0cube]

At a glance it seems like there works be a way to form the weighting adjustment into a subquery and multiply by the reciprocal. And then it’s still multiply-accumulate, but maybe I’m missing something.

[CWuestefeld]

Isn't that just saying "delegate the nonlinear part out to a black box"?

[h0l0cube]

At a glance (1 / (product_age_days * age_scale_constant)) just seems like another factor you can multiply (with the calculation of reciprocal costing more in compute time). Again, I'm more than likely misunderstanding you or lacking the context.