It's not the most accessible, but it's pretty interesting. Turning a problem into its dual has always been a bit of a dark art to me, but this article makes the benefits of doing so reasonably clear.
Turning a problem into a dual version is mostly a mechanical thing—you can immediately write it down in the case of LPs, for example[0], and many other convex cases [1].
Not very interesting. You see allocation problems solved all of the time in finance, and this blog post doesn't have anything new.
I also doubt that it's optimal. An implementation using Hierarchical Risk Parity for instance would be more interesting. I assume you can model risk/uncertainty here.
By the way, Google has an OR tools framework that implements these kind of solvers for you:
I think you are probably right, this does not seem optimal, it doesn't even seem that they recognized the class of problem that this is? It seems like they are attempting to use a greedy algorithm on a problem which very obviously has local extrema.
It seems like they are attempting to use a greedy algorithm on a problem which very obviously has local extrema.
Yep, this does not generalize well. You'll end up with a lopsided allocation, with one or two allocations holding a very large percentage of the total allocation.
Distributing the allocation weights could help, e.g. with ensembling, regularization or some other kind of penalization of large weights.
When i compared them a year or so ago, my conclusion that OR-Tools had more focus on engineering, making the whole thing easy to integrate etc, whilst COIN-OR had more sophisticated solvers, and gave you lots and lots of knobs to tweak.
Are there recommended resource for understanding and applying these type of problems? I am quite interested in optimization and its real life application, currently reading linear optimization by bertsekas
Uninteresting comment. You see critical, speculative comments that offer no technical insight on Hacker News all the time. I also doubt the article was read. Demonstrating actual effort and understanding would be more interesting.
How many HN readers read about OR before? Haven't read the blog post yet but it seems like a nice way to refresh my knowledge from some obscure lecture I attended about it ~5 years ago ;)
Since their budget is much larger than each individual incentive, isn't the greedy solution within epsilon of the optimal assignment? I.e. sort by v/c descending and take while sum(c) < B.
