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22:00
but it's kind of nasty doing that kind of computation
The definition is already numerically unstable to begin with
So I guess let’s not do that (?)
but I wanna see what it looks like x.x
@MaximilianJanisch ok, thanks!
@SimplyBeautifulArt You really are a special type of person 😄
well but cant you just increase precision to like 600 digits (apart from the symbolic computations)
>:P and what's that supposed to mean
(but I don't wanna)
kek
22:10
can any1 try to answer this...math.stackexchange.com/questions/3488809/…?
@SimplyBeautifulArt you really remind me of the quote „oh boy, that‘s a good sequence“ by Neil Sloane (see here youtu.be/etMJxB-igrc?t=167 )
@Hawk If I understand correctly you need to prove that all eigenvalues of the $T$ have modulus $1$ ?
Nevermind that is garbage
No I just don't understand why we are starting with the eigenvalues of T instead of \sqrt{T*T}.
I don't even feel like we need self-adjointness here
22:26
Okay why do we have $T^*Tv=\lambda_T\overline{\lambda_T}v$ @Hawk
Because $\lambda$ are the eigenvalue of $T$
But I don't understand how you know a priori that $v$ is an Eigenvector of $\sqrt{T^*T}$ and also $T$
So we start with \sort{T^*T} first
it has eigenvalue/vector pair (s,v)
then apply \sqrt{T^*T} to both sides of \sqrt{T^*T}v = sv
so s\sqrt{T^*T}v = s(sv) = s^2 v
LHS is now T^*Tv = s^2v
22:29
Yes
Hello alternative @TedE
Ah i think i see where ppl are confused and the downvote is for
cuz now I claim Tv = lambda v
Yes
that's why I am confused as well
ah okay that's why we need Spectral theorem
But I am still confused
Nevermind actually I get the idea now
because if $T$ is self-adjoint then $T^*=T$
@Thorgott polynomial multiplication with x=10 is basically multiplication
22:31
I think my book actually dropped the Self-Adjointness in the later edition
What book is it @Hawk
Nevermind
I think it is enough that $T$ is normal
Then by the spectral theorem we have the unitary diagonalization $T=U\Lambda U^*$
So $\sqrt{T^*T}=\sqrt{U\Lambda^2 U^*}=U\lvert T\rvert U^*$
Its Axler's book
Actually I am still not sure if my argument works in infinite dimensions
@AkivaWeinberger ok
How does the Hilbert space of your $T$ look like @Hawk ?
I think my argument works if it is separable
22:47
Multiplying a polynomial with a constant isn't all that convolve-y anymore
@MaximilianJanisch you certainly don't have an orthonormal diagonalization of an arbitrary self adjoint operator on a Hilbert space unless you change what that means lol. think of the left-shift operator on ell^2, which has spectrum every complex number with absolute value less than 1.
this is clearly a finite dimensional question, confirmed by the fact that it's from axlers book
@MikeMiller You are right
there might even be a counter-example in infinite dimensional spaces
thats a keyboard slip :D
Here is what I meant to say: When $X$ is separable and $T:X\to X$ is self-adjoint, linear, then $T$ is unitarily equivalent to a bounded linear operator $M_\phi:L^2(Z)\to L^2(Z), f\mapsto \phi\cdot f$ for some measure space $(Z,\mu)$ and $\phi\in L^\infty(Z)$
So my $\Lambda$ gets replaced by the $M_\phi$
($X$ is a Hilbert space of course)
23:07
@Thorgott no, I mean the indeterminate variable x being substituted with 10
I said x=10, not x-10
if you've heard of the "Japanese multiplication method", it's basically that
23 x 12 = 276
corresponds to
(2x+3)(x+2) = 2x^2 + 7x + 6
when you substitute x=10
ah, that's what you meant
yeah, that makes a lot of sense
@MikeMiller I see where the confusion came from: The singular values are only defined for compact operators
That works fine so long as none of the new coefficients exceed 10. But then one could do x=100 instead
So I can use the spectral Theorem in a very similar way to the finite dimensional case
When it works, tho, it does give an elementary example of how polynomial multiplication / discrete convolution
What I wonder now is the simplest way to introduce convolution of continuous functions
23:19
@Semiclassical I mean the idea is the same
you shift and dot product
I guess you could take a discrete convolution, view it as a Riemann sum, and then get an integral out of that
I don't think that the following question is a duplicate of the one it was marked a duplicate of, so I suggest you vote to re-open it, even though the questions are related.
2
Q: Does $f(x)\,dx$ denote multiplication of $f(x)$ by $dx$?

EmoIn the integral form $\int \! f(x) \, \mathrm{d}x$ does $f(x)\,\mathrm{d}x$ can be seen as a multiplication of $f(x)$ and $\mathrm{d}x$?

The usual motivation of the standard infinite-dimensional inner products is also as "continuization" of the finite-dimensional ones via Riemann sums, so that works out to be the same.
I think it would be more a duplicate of the following question (but I don't think they are strict duplicates, anyway).
49
Q: Is $dx\,dy$ really a multiplication of $dx$ and $dy$?

EmoOn the answers of the question Is $\frac{\textrm{d}y}{\textrm{d}x}$ not a ratio? it was told that $\frac{dy}{dx}$ cannot be seen as a quotient, even though it looks like a fraction. My question is: does $dxdy$ in the double integral represent a multiplication of differentials? The problem than ca...

23:21
@nbro Did you already post it in the CRUDE chatroom?
@Simply How is your tetration going
my tetration plugs the height into an exponential function, which implies it's periodic in the imaginary directions
No, I was not aware of that chat and I didn't even check it, of course
Another way is via integral transform theory , which would seem like overkill if not for how useful the convolution theorem is
People say that dx/dy is not a quotient, but then this quotient is used in many proofs, etc
it's not.
23:23
@nbro What?
Understanding integral transforms before convolutions seems like a daunting, if not circular, task
umm, what?
In mathematics, tetration (or hyper-4) is an operation based on iterated, or repeated, exponentiation. It is the next hyperoperation after exponentiation, but before pentation. The word was coined by Reuben Louis Goodstein from tetra- (four) and iteration. Under the definition as repeated exponentiation, the notation n a {\displaystyle {^{n}a}} means a a...
It's somewhat similar to this
i knew the definition of the Laplace transform before I knew about convolutions
hi @ted (s)
23:25
Hi @Semiclassic
but the thing there claims to only work for bases greater than e^(1/e) which is precisely when mine does work
you mean doesn't work?
Yeah, I learned Laplace transform freshman year in differential equations, and convolutions only in graduate real analysis later, as I recall.
Hi @Semiclassical
I'm seeing double.
23:26
er yeah
@Fargle !!
mine works for bases less than e^(1/e)
@Fargle Four Teds?!?
Yes, there's an impostor alternative Ted now.
Yeah, we should really deal with him.
23:27
seeing double and feeling single?
Where have you been, @Fargle?
What is feeling, but a different way of seeing?
I will not abide these personal attacks (yes)
Going through the rigors of a finance master's program.
-_-
23:28
What is so rigorous there? :D
:rimshot:
rigor mortis
bean counting
Semi has it right. But most of my capital-R research has been on stochastic differential equations.
23:29
Fair enough then. I haven't even learned the Laplace transform. Convolutions popped up naturally in probability theory.
laplace transform = characteristic function
It's something to do. Pretty far afield from what I tend to enjoy, but, hey, gotta diversify. Especially in this economy
$F(s)=\int_0^\infty e^{-s x}f(x)\,dx$ @Thorgott
Oh ... just wait for the economy next year!
Well, anyhow, I've missed you, @Fargle.
23:30
It's basically just a stepping stone to doing a pure math Ph.D without a 2.*mumble* GPA
Missed you too, Ted. :)
d/dx can be seen as an operator and dy/dx as the application of such operator to y.
Maybe some day I can do real math again.
I have nothing against applied mathematics.
so the laplace transform of a function $f(x)$ is literally just the characteristic function $E[e^{-s X}]$ with respect to $f(x)$ as the 'pdf'
lol, today I learned...
23:31
@TedShifrin +1
However, we often see the manipulation of dy and dx to move from one side of an equation to the other side, saying that dy/dx is not a ratio is at least misleading.
One of my favorite teaching experiences (and popular with a number of the students) was a year-long applied mathematics course. I worked my butt off teaching that course.
Granted, that was 1986-7.
@nbro: You are, after all, the expert.
No, I am not an expert and, of course, you're being sarcastic (or trying to provoke me), because you know I am not a mathematican.
this makes the convolution theorem for the laplace transform rather obvious then, doesn't it
tbf, when we write stuff like $y\,dx = x\,dy\implies dx/x=dy/y$, we are essentially dy/dx as though it's a ratio
but that's not really to be taken literally. it's a shorthand for manipulations at the level of the integral. (so long as one isn't talking differential forms, anyways)
23:33
But I always smash something in my room when I see this notation @Semiclassical
In the context of nonstandard analysis, it's a bona fide ratio. Otherwise, it is not. Learn about chain rule and differential forms.
@Semiclassical You manipulate it like a ratio and many expect people not to be confused or maybe they expect it but they simply don't care, whoever they are.
it's definitely an abuse of notation, but it's such a useful one :P
Fair enough
Differential forms explain perfectly nicely why $df = \dfrac{df}{dx}\,dx$.
23:35
It's meaningful if you treat everything as a form
When substituting in integrals even I use it in some sense
Hi @TedShifrin
That's the chain rule, @Maximil.
heya a @Balarka.
I slept for 18+ hours
23:35
@BalarkaSen nice
Felicitations. (Put the é if you want it in French.)
I prefer $y~\mathrm dx=x~\mathrm dy$ meaning $y\frac{\mathrm dx}{\mathrm dt}=x\frac{\mathrm dy}{\mathrm dt}$ for whatever $t$ is
There need be no $t$.
or rather you can always use $x$
@TedShifrin True, I like the german word Transformationssatz for the generalization to multidimensional integrals
maybe not
Time to study some math
I prefer understanding that (at the right level) as $\omega = y\,dx - x\,dy = 0$ giving a differential equation.
Have to decide what to study
If $X$, $Y$ are independent with pdfs $f,g$, then $X+Y$ has pdf $f\ast g$. So $\mathcal{L}\{f\ast g\}(s)=\mathbb{E}[e^{-s(X+Y)}]=\mathbb{E}[e^{-sX}]\mathbb{E}[e^{-sY}]=(\mathcal{L}\{f\}\cdot\mathcal{L}\{g\})(s)$.
23:37
Actually I don't even know what to call it in english
that is not Lapl... whatever
one way to avoid the issue: $$\frac{dy}{dx}=\frac{y}{x} \implies \frac{1}{y(x)}\frac{dy}{dx}=\frac{1}{x}\implies \int \frac{1}{x}dx=\int \frac{1}{y(x)}\frac{dy}{dx}\,dx = \int \frac{1}{y}dy$$
I'm going off of what Semi said :p
@Maximil: Change of Variables Theorem.
@TedShifrin Thanks 🙂
23:39
So one can avoid using differentials if one finds it distasteful,
In measure theory (particularly, in the context of the Lebesgue integral), $dx$ in $\int f(x)dx$ is a measure. Now, this clearly explains why you can take an integral with respect to a probability measure. However, some people claim that $dx$ is just notation or means infinitesimally small, but this fails to explain why $dx$ is a measure.
but meh.
I remember being confused before learning the theorems that there are two rather different theorems which are both called Change of Variables/Transformationssatz
@nbro If you want you can write $\mathrm d\lambda(x)$ or something like that where $\lambda$ is your Lebesgue measure
@MaximilianJanisch Are you saying that $dx$ is a shorthand for $d\lambda(x)$?
23:40
Yes basically
I was mis-remembering a bit earlier: I should've said the moment-generating function, not the characteristic function
I mean some people like to write $\lambda(\mathrm dx)$
hi @ÍgjøgnumMeg
and other stuff
(and there's an extra minus sign in the exponent because of that)
23:41
Hey @Leaky
@MaximilianJanisch Yes, I noticed that
I have exercises to prove "Kronecker-Weber for quadratic fields" rofl
@ÍgjøgnumMeg that's a fun exercise
@TedShifrin what do you expect the economy to be like next year?
Of course, this Lebesgue integral was developed after the Riemann integral, so I guess that the notation dx does not always mean that is is a measure. It can also be "just notation"
23:42
Characteristic function is the Fourier transform, which is $\Bbb E[e^{itX}]$.
@Leaky yeah? I'll have a go in a bit
@ÍgjøgnumMeg that could lead to quadratic reciprocity
can't be bothered atm rofl
but maybe you know it already
23:42
@BalarkaSen Learn surgery theory
@nbro it's a notation that was given alternative meanings
@skull: Of course I am as far from an expert as one can be. But for various reasons, I believe (and hope fervently) that it'll have a serious downturn for months preceding November.
@MikeMiller That might be fun. What's a good reference?
Wall's book
I think
That's huge tho
23:43
I don't know surgery theory
I was 99% sure he'd say Wall.
@BalarkaSen Oxford Handbook of Clinical Surgery, by Greg McLatchie
smacks Leaky
2
@TedShifrin I tend to agree
It's the only text I know, lol
23:44
In any case, you can always say that dx is a measure, for every integral, because the Lebesgue integral is a generalization of Riemann's one (and I believe that the Lebesgue integral is the most general definition of an integral that we have developed, or am I wrong?)
One should so the second edition, edited by Ranicki
Ranicki will have a copy in his site I am sure
@nbro If $f:\mathbb R\to\mathbb R$ is Riemann integrable on a compact interval then it also Lebesgue integrable on that interval and the both integrals coincide
a @Balarka: Maybe this will be germane.
Ok its in Schmuel Weinberger's site
23:45
d-anything is never a measure. You write, say, $\mathrm{d}\mu$ if you integrate with respect to a measure $\mu$. The notation $\mathrm{d}x$ gets an explanation in differential geometry as far as I know, but I'm not qualified to talk about that.
Actually the suggestion to read Kervaire Milnor and its predecessors is good @BalarkaSen
Also, in Euclidean spaces, the Henstock-Kurzweil integral supercedes the Lebesgue integral
@Thorgott True
But I like Lebesgue more :D
I went through my entire mathematical career/life and never heard of Henstock-Kurzwell until about a year ago here.
3
@Thorgott Well, as someone wrote above, dx can be thought of a shorthand for $d\mu(x)$
23:47
@TedShifrin I think I first mentioned it to you
I think that's quite likely.
Did you see the MO link on your surgery reference query?
I said that people write $$\int f(x) \,\mathrm dx$$ sometimes when they mean the Lebesgue integral $$\int f(x)\,\mathrm d\lambda(x)$$
Yeah, I did, Mike suggested their Kervaire-Milnor suggestion is better
Yes, I was referring to your comment then (I am lazy enough not to check it again above)
@nbro maybe you shouldn't get lost in abstractions (i.e. notations) and you should understand the intuition behind the concepts and connect them
23:48
Lebesgue integration is the richer theory overall, because the spaces are nicer.
Wall's book is difficult
@TedShifrin In my opinion the Henstock-Kurzweil idea is very creative
@LeakyNun lol
Henstock-Kurzweil has the cool property of being able to integrate any derivative though
To be honest I forget the various references KM will use (lol, KM) so you'll have to glance through to see what you need to read first
23:49
There are so many fucking integrals
The McShane integral
The Pfeffer integral
damn if theres anything ive ever wanted to do its integrate any derivative!
The nobody-gives-a-rats-ass-about integral
@MaximilianJanisch Are your parents mathematicians or something? How did you manage to start a master's in math at only 16 years old?
Feynman path integral, which is really another can of worms entirely
Also, dx can be thought of as a shorthand for dmu(x), sure. It's a symbol that even works without any inherent meaning in Riemann integration. I think the proper understanding, which, in the cases you are looking at, will be consistent with the Lebesgue theory is explored in differential geometry, but, again, I can't say anything on that matter.
Isn't McShane equivalent to Lebesgue?
23:50
@TedShifrin You may not, but I tire of finance in particular.
Or was that Daniell?
In my mathematical career, the Riemann integral (in the context of integrating on manifolds or real analytic spaces) has been sufficient, although in terms of reading papers and some teaching, of course, I have needed the Lebesgue integral, for distributions and currents, etc.
predictive policing has its problems too
@Fargle: I've never studied mathematical finance, tbh.
@nbro It is quite a long story. Basically I started studying mathematics while still in high school so at the end of high school I was almost done with the Bachelor
23:51
A lot of it is just stats and stochastic dynamics.
@Thorgott Shrug, I only know Henstock-Kurzweil because I read it in Folland's analysis book once
ooh, Folland
Fokker-Planck and all that
I know a number of pure math PhDs who went to work on Wall Street or other avenues of math finance.
im sure
23:52
@BalarkaSen have you ever looked at the historical definition of the Henstock-Kurzweil integral?
@LeakyNun It's a good one right?
No @Thorgott
Lot of faffing about with Itou's lemma and so on.
@BalarkaSen the harmonic analysis one right
Wikipedia says it was done with transfinite induction over the number of singularities
I'm not sure what to think of that
@LeakyNun Oh no the "Advanced Analysis" thingy
23:52
@MaximilianJanisch Well, I've read in your dedicated Wikipedia page that your father was a mathematian. Anyway, this is very impressive!
which is basically a compression of everything a graduate student needs to know about analysis
Ito’s lemma is firmly in the nope-I-dunno category for me
@nbro Yes, he is still a math professor (now emeritus)
its cool to try to make sense of all variations of the same notation as if they're all commensurable and appeared in the same historical context imo
23:53
I should also eventually read Falconer's little book on fractals
tip: only read tough books after 18+ hours of sleep :P
supposed to be a good entry drug to GMT
It's basically just the statement that "you must account for second-order variation in the chain rule when dealing with stochastic stuff".
@MaximilianJanisch Well, I guess you're the only person I've ever encountered to which I don't have to say "good luck", so I won't say it :)
23:54
(It was McShane, which is equivalent to Lebesgue)
@nbro Haha 😄
It falls right out of a Taylor approximation but I don't have the stuff in front of me. It being break and all
@BalarkaSen how about read Dummit and Foote
Lazy bum, break and all @Fargle :D
I could
23:55
I’ll note now that my experience with SDEs is to use the WKB approximation to get a PDE
I could just go back to Galois theory
do it
Balarka needs an algebra-free zone.
Most of my experience is numerical, if that makes you feel any better, Semi. I'm not out here trying to get analytic PDFs.
no he doesn't
23:56
@BalarkaSen if you do algebra ill be forced to
Just trying to work on better integration methods.
I promised I would read a substantial amount of Galois theory before the break gets over so I guess I should do Galois theory
Like which ones? @Fargle
@Fargle well, so was mine
23:56
good decision
Whom did you promise, a @Balarka?
@MikeMiller Let's hop in the algebra wagon
1 min ago, by Leaky Nun
@BalarkaSen how about read Dummit and Foote
@TedShifrin Nobody, that's the trick :P
23:57
Precisely.
Mostly because the KPZ equation, in the weak noise approximation, gives you a pair of coupled nonlinear pdes
OK, I will spend the next 3 hours finishing all of Galois theory
nice
I guess I'll be here answering your questions
How nice of Leaky to nominate himself as the expert-on-high.
Not holding out much hope of solving those analytically, especially when the boundary conditions are as miserable as they were
23:58
I guess I'll be here to lurk and learn
@MaximilianJanisch Btw, I've been to Luzern once. It's pretty nice there (apart from the train station and the surroundings). However, when I went there, it was a bad situation and moment, so maybe this is a biased impression. Anyway, I will never forget that place
@BalarkaSen So long as you're done afterwards
@TedShifrin I didn't say I'm the expert
@MaximilianJanisch Stochastic stuff doesn't generally do well with stuff like implicit methods---take, say, Adams-Bashforth-Moulton, properly generalized---because they derive future and current estimates simultaneously. But there are some nice matricial formulations of some high-order explicit methods that end up not being terribly computationally expensive.
The stuff I did was in this paper: arxiv.org/pdf/1606.08738.pdf
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