I don't have a CS degree, but I'm pretty sure everyone who does was supposed to have learned what Ω means in this context. My understanding is it's standard wit

I don't have a CS degree, but I'm pretty sure everyone who does was supposed to have learned what Ω means in this context. My understanding is it's standard within the field, and is a standard part of undergrad CS programs. So I'd expect that someone building libraries for data structures does understand it (or could refresh their memory). And again, with something like Ω, if you don't understand it, a glossary will not help you. It's not going to help to see "f = Ω(g) means `lim inf n->infinity f(n)/g(n) > 0`". Keep in mind this is a paper, not a tutorial on complexity theory. > Yes, that's one of the many reasons why it's a terrible practice. The same symbols can mean different things across papers. They can just like homonyms in other languages, but the meaning is usually clear from context. If you see "probabilitly 1-δ", you can expect that means "close to 100%", and probably "as close as you'd like" by tweaking some parameter that affects δ. Where there is ambiguity, the author typically spells it out. In longer works like books, authors often have an intro chapter/section where they list all of their notation even if it's completely standard with no ambiguity.