serenity4
commented
11 months ago Yeah, I don't know how computer scientists typically go about justifying claims of time or memory complexity. I think that typically, in the case of the iterative Julia implementations, it's quite obvious and can be typically inferred from how many for loops you have and over what. But when caching is involved (which is the case in many real-world domains outside of math-y algorithms) as is the case of memoization-based implementations, then things get more complicated. We could investigate how other computer scientists would approach the problem, e.g. maybe read research articles on implementation domains that face similar challenges.
We have an approach for Big-O notation memory complexity in the case of our iterative Julia models - #34. I wanted a way of doing this for lifelib models and had an idea I thought would work, but it doesn't seem like we should do it - #49.
We want to classify or categorize different common approaches to actuarial modeling by this Big-O notation.
It is possible that we simplify the model while maintaining the same fundamental structure and then sort of just make an appeal to logic/math in the way that a computer scientist might. Then the argument for big-O notation isn't dependent on a specific implementation?
Anyhow, we don't have argument that the lifelib models should satisfy certain properties. Like Seriatim having different memory complexity than vectorized is sort of obvious in some sense, but saying it in the right way might be necessary to really have done the topic justice in my opinion.