Consider a set of fuzzy (partially true) truth values $T=\{t_0,t_1,t_2,...,t_n\}$ with a partial map $\psi:T\to J.$ Where $0\le t_0,t_1,...\le1.$ How do you describe the notion of the target space having a phase space of possible values?
For all the values of $n$ I've tested, it comes out to 1, actually
But it's been a while since I've done any real calculus so all my testing has been with a computer, and it feels like this'll be tricky (it might not be, of course)
In mathematics and physics, in particular differential geometry and general relativity, a warped geometry is a Riemannian or Lorentzian manifold whose metric tensor can be written in form
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I would like to understand the first section of a paper titled: "More on Convolution of Riemannian Manifolds," by Bang-Yen Chen. Michigan State University.
http://emis.impa.br/EMIS/journals/BAG/vol.44/no.1/b44h1chn.pdf (first two pages).
The notion of convolution products is defined as follows:...
hey all, once again. I've started realizing I probably won't go back to university (given doggo, full time job, and living in slovakia), but also that I have a lot of free time I'm looking to fill up with things I find fun. So I thought it could be interesting to go over the lecture notes and exams of the various lectures from MIT, in order (disgarding outdated ones): ocw.mit.edu/courses/mathematics
is this a crazy thought? I'm a developer for a living, and I'm also trying to minimize screen time because I get too much of it from work, and my eyes can start hurting
would I be better off buying a text book and going through it?
in highschool I couldn't focus on textbooks at all. I did 1 year of uni, on computer science, and all we needed were lectures, which are a format that works for me. But I don't like the idea of spending my free time watching videos
so I'm not sure what alternatives could fit my needs
ideally I'd find a textbook that makes you think most of the time, and after you've thought, the book tells you how a problem is conventionally approached, poses another problem, and repeats the process
this should be able to fit on my phone, with me spending most of my time scribbling notes on paper and feeling productive, right?
Sanity check: $k[X,Y]$, $k$ a field, is noetherian and factorial, but e.g. $k[X,Y]/(Y)$ is an integral domain and not a field, so $(Y)$ is a non-maximal prime ideal, i.e. $k[X,Y]$ is not a Dedekind domain.
Dedekind domains, as I recall, intuitively correspond to coordinate rings of curves ... so of course the coordinate ring of the affine plane will not be one.
I'm not asking for $-\frac{1}{\omega_0}\le \frac{\sin(2 \omega_0 T) \cos(2 \theta)}{\omega_0} \le \frac{1}{\omega_0}$ in isolation. That is the problem. I don't know how he jumps to the answer
@TedShifrin you are right but this is different problem.
The point is that
$$
-\frac{1}{\omega_0}\le \frac{\sin(2 \omega_0 T) \cos(2 \theta)}{\omega_0} \le \frac{1}{\omega_0},
$$
and this is true for all values of $T$. Therefore
$$
\lim_{T\rightarrow \infty} 2 T+\frac{\sin(2 \omega_0 T) \cos(2 \theta)}{\omega_0}=\lim_{T\rightarrow \infty} 2 T
$$
and
$...
But it's clear that the answerer was applying it. If $a\le f(t)\le b$, then $\frac aT\le \frac{f(t)}T\le\frac bT$, so as $T\to\infty$, since $a/T$ and $b/T$ go to $0$, so does $f(t)/T$.
Someone posted an answer to the question I asked for help on yesterday. I started working through the posted answer and got stuck on the second line. I asked the answerer how he arrived at it, and ultimately he realized it was wrong and removed the answer.
@Edward Dedekind domains have Krull dimension 1, so they are one dimensional affine schemes so it makes sense that they should be curves or something close even though I don't know the details of this story
@TedShifrin true. I've read the entire chapter about limit in Calculus by Steward but hard and practical problems usually are not covered. Meanwhile, I'm reading some signals and systems and these problems pop up.
This is all elementary with the squeeze principle (theorem), which is certainly covered in the calculus books.
But notice that someone else posted an answer after mine, totally ignoring the ("rigorous") question of the difference between all $T$ and all integer multiples of the period. That's why I wrote that stuff carefully for you. But in practice signal engineers won't ever worry about that.
It seems the guy uses a theorem that says if a signal is periodic with fundamental period $T_o$, then the normalized average power P of the signal is the same as the average power of the signal over any interval of length $T_o$.
I like a rigorous approach like the squeeze theorem.
i have a function defined by $ f(x)=[x]+\sqrt{x-[x]}$ where [.] is the floor function, I proved that $f([k,k+1[)=[k,k+1[$ for all k in Z, and f is differentiable on R\Z, how to deduce the monotony of f ?
How rigorous a proof do you need? $\sqrt x$ is increasing on $[0,1]$ and the graph makes it clear that we shift up one unit when we go to the next integer, so that it continues to increase.
On any interval $[n,n+1]$, the fact that $\sqrt x$ is increasing does it. If $a<n<b$, then $f(a)<n<f(b)$ does it.
Yup, still doing GGT and stuff related to mapping class groups. I am alright, tired of typing and went to see what was happening here. Doing set theory still?
Kinda, I ran out of set theory courses I can take and I'm not doing set theory for my thesis since the logic group was disbanded following the retirement of its only professor
Oh actually, I'm doing some stuff somewhat close to GGT for my thesis, about asymptotic dimension of metric spaces, so far I've been reading some work by Dranishnikov mostly
Oh, this is your masters still? Cool, I have looked a bit at asymptotic dimension, don't much. There is actually this space I thought about for a tiny bit that I think should have infinite asymptotic dimension but haven't really put the time to formulate an argument (in part because it would only be interesting if that ended up not being true)
Basically there's a weird compactification of proper metric spaces which is called the Higson compactification and Dranishnikov proved that for proper metric spaces of finite asymptotic dimension the asymptotic dimension of the space coincides with the Lebesgue covering dimension of its reminder in this compactification
My thesis advisor used this result to prove that if you quotient a proper metric space by a finite group acting by isometries the space and the quotient have the same asymptotic dimension, but he was using Dranishnikov's result as a blackbox so he asked me to learn how that works
If you are interested it is the ray graph for the plane minus a cantor set: vertices are simple rays from and to infinity up to isotopy, edges between if there are no intersections between them(up to isotopy).
Eh, you don't really have to think about it too much, just you are allowed to move the ray around in the obvious topological ways (or you can think of homotopy) as long as it goes to infinity (or you can think of it as a sphere minus an isolated point and a cantor set and the rays go to the isolated point)
I don't think I understand the without intersections part, in the sphere picture can't I make any two such rays cross close to the isolated point? The missing cantor set is contained in a compact subspace so it won't be an issue for those homotopies
oh, forgot the definition (there is a related graph like the one), one of the points starts at infinity the other end of the ray ends on the cantor set
Oh yah, there are similar graph even just on closed surfaces (the curve graph), which are locally infinite and stuff like that. Somewhat surprisingly they end up being unbounded (including the above ray graph) and having interesting geometric properties like being gromov hyperbolic
The finite type ones have finite asymptotic dimension
That's going to be part of it for sure, I already typed up half of the result (Dranishnikov proved the two inequalities separately a few years apart from each other)
But maybe I'll also have something more to put in the thesis, we'll see
@geocalc33 let's choose one problem from section 1, I'm finding those most difficult. Then in section 2 (chapter 1) things become more abstract / nicer to do math in. So I will work all odds in section 2, 3, ...
chapter 1 I mean
Manifolds are definitely an interesting topic. They're used in physics as you know
Configuration spaces for bots and spacetime fabric
I gave up on BananaCats for now. I'm leaning toward writing a Lean IDE if anything, but that math is sooo hard (type theory stuff)
Prescribe a space $Q,$with algebro-geometric objects (polynomials embedded in $\Bbb C^2$) $$ p_0,p_1,...$$
which form a ring $R[x]$ over the complexes. Actually, an algebraicly closed ring.
Assume there exists a duality between $Q$ and $F,$ another space. Denote this as, $Q=\mathscr {dual}(F).$...
@AlessandroCodenotti In undergrad I took a topics course on combinatorics of open covers which involved topological set theory (I think it was basically a topological set theory course with a specific focus)
In particular, maps from CC to itself should be isomorphic to maps from RR^2 to itself. It’s just that the kinds of maps one studies the former case may not look terribly nice when expressed in the latter case
You would do this. Let $p_1, p_2, \dots$ be an at most countable collection of complex polynomials in the variable $x$. Define the ideal $I = (p_0, p_1, \dots)$ . Then in addition to being an ideal of $\Bbb{C}[x]$ ring, $I$ is itself a ring (all ideals are also subrings by def.)
In mathematics, especially in the field of algebra, a polynomial ring or polynomial algebra is a ring (which is also a commutative algebra) formed from the set of polynomials in one or more indeterminates (traditionally also called variables) with coefficients in another ring, often a field.
Polynomial rings occur in many parts of mathematics, and the study of their properties was among the main motivations for the development of commutative algebra and ring theory. Polynomial rings and their ideals are fundamental in algebraic geometry. Many classes of rings, such as unique factorization domains...
I think that says somewhere that $K[X]$ if it's a Euclidean domain then it's a PID, and complex polynomials definitely have division with remainder algorithm, so form a PID. Not 100% sure though
This is my problem. I dont understand this. When I'm doing a limit in complex variable how can I understand the result? I know that a radix has more than one result.
If you have a well-defined branch of the function that doesn't have a branch cut along the ray in question, then the function is continuous, so who cares about limit?
At any rate, the limit is pointless here: You have a continuous function. You should understand where that stupid limit formula comes from (I never taught it or used it).
A vector field is like the continuum of vectors pointing inward toward the Earth (an inverse square-law vector field). But field in abstract algebra are scalars (where as a vector field is a collection embedded in an ordered n-tuple of scalars). So you can't usually divide vectors but a field in AA by definition has inverse in both + and . ie. you can divide.
You CAN divide in $\Bbb{R}^2 \approx \Bbb{C}$ which is what you're noticing
You should understand residues from Laurent series. And you should know what the residue of $f(z)/g(z)$ is at $z=a$ when $z=a$ is a simple zero of $g$.
@Thorgott: It leads to students' memorizing and not knowing what the hell they're doing. So my experience of 40 years taught me.
I know definition of residue so that it is "linked" with Laurent series, but I don't understand how use the formula, i don't understand what is the result of the limit, because a radix has more than one "result"
It does not have more than one result once you've chosen a well-defined branch. You have to understand that before you start computing contour integrals and residues.
(another qualifier: you do have physicists who use “fields” in the proper diff geo sense, but those tend to be either GR people or mathematical physicists)
ABC, they say if you ran into a hard problem there are a few easier problems (lower hanging fruit) that you can solve. I would look at the problems that build up to the result you're struggling with
@Semiclassic: There is an answer posted to that question. The answerer had a mistake the first time, but now I think it's right. But we end up with an ODE we cannot solve other than numerically.
There is a primality testing algorithm that is revolutionary solving a 100+ year old problem called AKS primality test. Uses 15 pages of beautiful abstract algebra & group theory to solve.
@TedShifrin Ok I understand now ... at least I think so. But I don't understand now, why if I do the sum of residue I don't get a real number but a complex number