I am solving the following problem from AM commutative algebra. I am solving commutative algebra in my spare time to get good grip in it.
Prove that the Zariski topology is Quasi-compact. Here is my proof, I just want to make sure that I have everything correct.
We want to prove that X is quas...
@BalarkaSen Yeah if you think about it most human knowledge is really based on analogies. Some discoveries might not be even discovery in the end, but analogy related to something else.
I would definitely recommend Surfaces and Essences by Hofstadter and Sander for a more detailed description of knowledge-as-analogy, coming straight from a cognitive scientist horse's mouth.
id like to take a whack at Topology again (first time i tried it really didnt make any sense ) anyone reccommend what i should have an understanding of / book/s i should understand before giving it a whirl again?
I don't know if there's quite a material prereq (you'd need set theory but most topology books start with what you need) so much as a "mathematical maturity" prereq
i have a fairly firm grasp on advanced calculus on R or C and a fairly strong understanding of abstract algerbra now just wondering if theres anything else i should understand ( i know last time i really struggled with what a mertric space was)
@Faust7 You should have a decent grasp of the properties of open and closed sets in $\Bbb R^n$. Those properties (e.g. stuff about unions and intersections, complements, and so on) directly motivate the topological definitions.
As long as you have that, I'd say you should be fine.
@Faust7 Vector spaces contain vectors, and you can add them and scale them. A general metric space can have any objects, and you might not be able to add or scale them.
The thing about metric spaces is that you require that "distance" make some sense, in a way that corresponds to the way distance normally works in the real world.
If you have a vector space, you also have to specify what's called a field, which is those objects you use to scale. If that is $\mathbb{R}$, then you can put a norm on the vector space. You can always look at a norm as the distance between a vector and 0
Well, in non-Euclidean geometry, the idea is that you have a different metric, so that while the shortest distance between two points isn't a straight line anymore, the law is still true for the metric
Right. You ignore Euclidean distance and impose this one instead.
Anyway, back to my belabored point, when one is discussing topological spaces, one question you can ask is whether it's "metrizable", that is, whether there's a metric such that the structure of the space can be described solely by the metric.
@Faust7 Think of it as being analogous to binary operations from abstract algebra. Those take in two things in the set and give you back a thing in the set.
But now, instead of giving back a thing in the set, the function is giving you their distance, which we represent by a real number.
If every distance is defined to be 1 (except the distance between $x$ and $x$, which is always $0$ in any metric), what points are distance $< 1/2$ from $x$?
Basically, so you know how if you have a metric, you can start talking about open sets? Even if you don't have a metric, you can still grab a bunch of sets and call them open
we spent alot of time on odd things that can happen with open and closed sets and the concepts of continuity etc in various calculus courses and analysis
That's a topological space, give me a set and call some subsets open. Now, there are some requirements, and they mirror what's true for metric spaces (or normed ones, as you're more used to dealing with)
So the idea behind this generality is similar to most. You can prove a bunch of things in R^n, and you ask how much of the structure is needed to prove certain claims. If you don't need much, and then find you get a nice bunch of sets which also satisfy it, then :)
i find mathematics at least for me has been wrought with epiphanies that what was previously taken as fact; was at the very least a convenient misunderstanding or worst blatantly wrong.