#linear-algebra
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no mathematician sits down and just stares at a proof and suddenly figure out the steps in an instant
they do it by trial and error
there's a reason why all these stuff took thousands of years to build upon cause these patterns weren't easy to recognize in the first place.
i know professors and some of your fellow classmates makes you feel this way
but in reality they 100% did not sit down and just understood everything over night.
if they do then they're most likely exaggerating. the human mind is not made for these type of things.
that's why books exists to contain information.
I just don't understand why force everyone to learn this if it doesn't pertain to their field of study; This causes more stress and anxiety I ever felt before and trying to figure this out with a textbook that isn't very useful makes it even more difficult. I'm reading the chapter but it doesn't make it easier to understand how to find the answer to the math problem
universities just wants your money
there's a reason why people call it garbage
but to make garbage into gold you have to own up to it
make the most out of it by enjoying it.
i know it doesn't sit right but if you can enjoy the process it'll make things easier.
Would someone be able to help with showing well definedness for 2?
I think i have to show that if
$v_1 + W = v_1'+W$ then $<v_1+W,v_2+W> = <v_1'+W,v_2+W>$
Victoria:
To show this is well defined, consider $\langle v_1+W,v_2+W \rangle'$ and $\langle v_1'+W,v_2+W \rangle'$ such that $v_1 + W = v_1'+W$ . Then, we have to show that $\langle v_1+W,v_2+W \rangle' = \langle v_1'+W,v_2+W \rangle'$ But since by definition of equal cosets, we have that $v_1 - v_1' \in W$, so we have that $\langle v_1-v_1',v_2 \rangle = 0 = \langle v_1,v_2 \rangle - \langle v_1',v_2\rangle = \langle v_1+W,v_2+W \rangle' - \langle v_1'+W,v_2+W \rangle'$
does this work?
Victoria:
also I'm kinda stuck on proving that the new inner product has the property of positive definiteness where <w,w> = 0 iff w = 0
Positive definiteness: We just have to show that $\langle v_1+W,v_2+W \rangle' = 0$ iff $v_1+W = v_2+W$ as nonnegative is already given to us by our original function.
$\xrightarrow{}$ direction:
$\langle v_1+W,v_1+W\rangle' = 0 \xrightarrow{} \langle v_1,v_1 \rangle = 0$ Let $w_1 \in W$ then $\langle v_1+w_1,v_1 \rangle =\langle v_1+w_1+W,v_1+W\rangle'$ However, we showed in well definedness that this holds iff $v_1 +W = w_1 + W \xrightarrow{} v_1 + W = W \xrightarrow{} v_1 = 0$.
Victoria:
i think this is right? Im honestly not too sure
can I get help understanding this bit regarding elementary row operations? thanks
I think one of the symbols is called kronecker delta? it was mentioned earlier but I dont know what it means
$\delta_{ij} = \begin{cases} 1 & i=j \ 0 & i\neq j \end{cases}$
Ann:
yes
is the inverse of a matrix A the same thing as the product of the elementary operations needed to get a matrix A to RREF
in other words does U = A(inverse)
also is the identity of a matrix the same thing as the RREF of a matrix
I can see why this exists but don't know how to get it?
@clever cedar not always, if square matrix A is full rank, then yes rref(A) = I
Like I think that it has something to do with the definition of a matrix fo a linear mapping?
my textbook explained the form of B = UA and said that B is the reduced for echelon form of A, and U is the product of all elementary matricies needed to get A to RREF. Therefore A * U = B can only be possible if U is the inverse, correct?
i should've stated that i assume the matrix is invertible
A = (1,2,3),(5,8,11)
this would not span R^3 but is there a possibility of it spanning a plane in R^3 ?
is the product of elementary row operations of A to A(inverse) the same thing as the inverse of A
@clever cedar if you take the product and multiply it by A, do you get hte identity matrix?
funny u mention that, i actually just found the product of elementary row operations of to A(inverse) and it was A
wait no
that cant be true
@clever cedar if you think something is the inverse of another thing, say u and v, then it should satisfy u*v = identity
ffs
thats exactly why this is confusing
for me
i cant understand why we care about elementary row operations
when we have that formula
So multiply out all of your elementary matrices to get a single matrix
If it satisfies the above, then you've found an inverse.
@north sierra
Those two vectors span a plane in R3, yes
okay
Two linearly independent vectors span a 2D space
but it wouldnt span all of R^3 right
Nop
There's many points in R3 that cannot be made with those vectors
thank you for the help these past few days
also to everyone else that helped
okay i see
Np, anything else you want to ask? You seem pretty confident
I'm not sure how to approach this question
So far my ideas are that uh
R^n -> R^n are isomorphic
And change of basis is linear
But that's all I got
try and draw a diagram
you have B[L]C going from (R^n,B) to (R^n,C)
and on a separate line draw C[L]B going from (R^n,C) to (R^n,B)
connect the two rows of the diagram with vertical arrows P : (R^n,C) -> (R^n,B) which is the change of basis and P^{-1} on the other side
Wait there's no P^{-1} though ?
instead of P^{-1} you can just draw P going in the other direction
one sec
More like this?
one of the top/bottom arrows points the wrong way
depending on what B[L]C means
otherwise yes
I'm not sure I understand what you drew
Um
here's what I have
top row: C[L]B going B to C
bottom row: B[L]C going C to B
left column: P going up from C to B
right column: P going down from C to B
right
the point of drawing this is just to organize our thoughts and see what is happening
it also suggests that the P you want to use to solve the problem is the change of basis matrix from C to B, just because that's what makes the diagram make sense
Ah
now to prove it you just have to take a vector in R^n and write it in C basis
Wait I think that this diagram is slightly wrong
Should be other way lol
I want to show that (P)(bLc)(P) = (cLb)
This diagram shows that (P)(cLb)(P) = (bLc)
It's the same thing though
ah yeah you're right
I thought the convention for the B and C subscripts was the opposite when I first looked at the question
so then you should take P to be the change of basis matrix from B to C
Yea
So if we have some change of basis matrix from B to C, multiplied by a change of basis matrix from C to B, multiplied by a change of abasis matrix B to C, we should end up with a change of basis matrix from B to C lol
Now how do I state that mathematicalyy...
what you said isn't quite what you want
because you referred to all 4 matrices as change of bases
but L is anything
stating it mathematically is just the equation given in the problem
you just need to calculate what happens to a vector written in B basis when you apply the composition of the 3 matrices
Hrm
Wait
Hrm
It says to prove that there exists a matrix P
Hrm
Like that wouldn't prove that a matrix P exists, would it?
we know there exists a matrix P that changes basis B into basis C
I assume we can take that for granted, yes?
Hrmm
I mean
THe question is basically saying to prove that such a matrix exists isn't it?
I think that's what they're trying to prove.
Well the book also gives that we have
how about using that definition on the identity map
By the identity map you mean L:R^n->R^n, L((x1 ... x_n)) = (x_1 ... x_n) right?
yup
If we use the standard basis with the identity map then we'd just get the identity matrix
then the property from the picture you posted is [v]C = C[I]B [v]B
so take P = C[I]B
yes that's true but we have two arbitrary bases, not the standard basis
yes
And if it's R^n->R^n, c[I]b must exist yea?
any linear map has a matrix in any basis
Yes
regardless of whether it's R^n or the identity map
so yes
here are the two things you know: $P[v]_B = [v]_C$ and ${}_C[L]_B[v]_B = [Lv]_C$
Seoin:
now you just need to use them to show that $P{}_B[L]_CP[v]_B = {}_C[L]_B[v]_B$
Seoin:
which will give the statement the problem asks for since you will verify that this is true for every v
Ah
everything make sense?
Um
Kinda
STill a bit muddlede
But uh gonna take a few minutes to try to think through it
And see if it makes more sense once I put it down on paper
if you understand everything I wrote
then to prove it you just need to do the calculation I mentioned
By the calculation you mentioned you mean $P{}_B[L]_CP[v]_B = {}_C[L]_B[v]_B$
Liria ^(;,;)^:
right?
yes
Ah, and (cLb)(v_b) = (Lv)_c because of linearity...?
Ah, no, it's a definition
Ok
yeah I mean
it couldn't really be any other way
Ah
Ok I think I see now
Like the actual math is kinda sketchy but like
It makes sense logically now?
what is sketchy?
Mmmm
I wrote it out like this
Like
Oh wait brackets are missing
But uh
I can't concievably see myself coming up with that in a short period of time haha
yup looks good
Thank you!
i dont understand of how this is a subset of the xz plane
i know you explained it before @half ice but i still dont really get it
what's the y component of those vectors?
mhmm
does the plane cover all of R^3
if I give you any point in R^3 with a non-zero y component
would it be in the plane
these are 3d vectors... they exist in 3d aka R^3
no it would not cover all of R^3
so it's a subset of R^3
if it was not a strict subset of R^3, then it would cover all of R^3
what does xz-plane refer to?
the plane that goes through the x and z axes
all points (x,y,z) where y=0
those aren't points in R^3
I mean it's still the xy plane, but it's just the xy plane in R^2
rather they're in R^2 where you have (x,y) coords
the xy plane in R^2, is all of R^2
always?
oh nvm
so xz plane in R^3 would be not be all of R^3 then right
itd be a subset of R^3
yes
ok
got it
thx !
to be more specific
wouldn't you say xz plane is a proper subet of R^3?
you could
hey i'm not really getting the intuition behind this
behind what exactly?
"If L:V->W is a linear mapping, L is one to one iff Ker(L) = {0}"
like yea the proof makes sense
but its not really intuitive to me?
Well if Ker(L) isn't just {0} then it clearly wouldn't be one-to-one
its not 'clearly' to me yet so thats why im asking
Well the definition of one-to-one is that no two elements get sent to the same element
0 is always sent to 0 in a linear map
So if anything else is also sent to 0, then your map isn't one-to-one
The other direction is harder. It turns out that if any output has multiple inputs, then the zero output also has multiple inputs
"iff" means this statement is really two statements
yeah
i'm just asking cuz i've been using the lemma
like so often
but i dont think i actually get it
like i get rank nullity for example
It's very useful yeah. You can check one-to-one-ness just by checking the zero output
for me, rank nullity makes sense but this doesnt really yet if that makes sense
its like
i know how to use it but it still feels weird?
idk if its relatable at all
It definitely is relatable. In this case, I would suggest trying to prove it yourself! It's not a very hard proof. You need the fact that the function is linear, and it's over pretty quick
ok thanks ๐
i tend to try and just hammer out examples but i think thats why i dont do well on the proof sections of tests lol
https://cdn.discordapp.com/attachments/622430759088816129/626421170102599689/image0.jpg Hi! I don't understand the simplification from
$E_{ik}A_{kj} = A_{rj} + cA_{sj} , i = r$
Spes:
when you sum over something with the kronecker delta it acts to "replace" the index that gets summed over with the other: $\sum_j\delta_{ij}A_{jk} = A_{ik}$; it's like we took the $j$ index on $A$ and replaced it with an $i$ using the delta
in that step, youre summing over k, so the first Akj becomes Aij and the second turns into Asj (then since i=r you can replace the subscript i with an r)
I'd like to understand why when a determinant for a homogenous matrix is 0, there are an infinite amount of solutions
Could someone explain it to me like I'm 5
a 5 year old wouldn't understand matrices
or determinants
or homogenity
or infinite solutions
Says the one with a pfp of someone who definitely would at 5

When I show the existence of the zero vector in a subspace, do I need to show that it is the result of the additive inverse property?
when the determinant is 0, at least one row is not linearly independent from the other rows. If you row reduce such a matrix, you will have an all zero row at the bottom. If you find the solutions, since at least one variable is dependent on the other variables, there ceases to be a unique solution
and you can vary the dependent variable(s) around to get infinite solutions
I mean if it's a zero vector in the main vector space
then it's still going to be the zero vector in the sub space
you just have to show it exists in the subspace
you already know it is the result of the additive inverse property in the main vector space, no need to re-prove it
you just need to prove the zero vector exists in the subspace (and you have to prove the subspace is closed under addition and scalar multiplication)
that's all
btw ishigami best girl
technically proving addition and scalar multiplication is closed already proves the existence of the zero vector in the subspace
but I like to independently check for the zero vector first
ok thank you
@cloud cedar why does summing over the kronecker delta "replace" the index that gets summed over?
write out the terms of $\sum_j\delta_{ij}A_{jk}$
and youll see that youre only left with Aik
since only the term where i and j are equal are kept, the kronecker delta function multiplies every other term with 0
yup
it's the same concept as integrating a function multiplied by a shifted dirac delta function
just discretized
I see, thanks
there is no solution
ah ty
the constraints on the solutions
don't match up
so the constraints are "inconsistent" (with each other)
another question: in future scenarios when I have trouble understanding a particular theorem, how should I visualize it to help me understand it?
depends from theorem to theorem
might not even be possible to visualize it fully
if it gets abstract enough
that's when you have to just trust the math checks out
which theorem
oh I see it
for this one you can just try writing out some simple 2x2 or 3x3 elementary row operation matrices
and see what happens when they multiply random matrices
get a feel for it and convince yourself it's true
hmm ok
for most matrix proofs, just try doing some examples with 2x2 and 3x3 matrices (or like 2x3/3x2/2x1/1x2/3x1/1x3)
to get a feel for it
bigger sizes get a bit unreasonable to do by hand
1x1 is just scalars
somewhat related question: in matrix multiplication AB, is the product a linear combination of B?
for example
take an elementary matrix E
1 1
0 1
and another matrix A
2 3
4 5
I guess what I'm really asking is
let C be a matrix such that AB = C, for two matrices A and B
The rows of C are in the row space of B
The columns of C are in the column space of A
is there a way to think of E as the identity matrix with a 1 in the 1,2 position, and how it affects the product?
does that answer your question
what is row space and column space?
a linear algebra textbook can probably explain it better than me
or someone else here
is it like all possible combinations of the rows of B?
yes
the rows of the product EA would be a combination of the rows of A, so that's why it's a row operation
you can indeed look at E and predict what row operation it would be
the elementary matrices are quite simple
all 3 elementary matrices are simple changes to the identity:
if E looks like the identity with 2 rows swapped -> row swap operation for those two rows
if E looks like the identity with one non-1 element along diagonal -> multiplies that row by that non-1 number
if E looks like the identity with one non-0 element outside a diagonal, then you're adding a multiple of a row to another
there's also column operations if you want to look into it
where it would instead be AE, and the product would do column operations instead
The rows of C are in the row space of B
The columns of C are in the column space of A
this is still a little fuzzy to me
you'll get to it sometime
alright
also wont the span of the rows cover the entire ... um... space?
like it could reach everywhere, right?
oh because they could be all missing a x_k unknown where 1 <= k <= n
there is a possibility there aren't enough linearly independent rows
to span R^n
or that there aren't even enough rows
like in the matrix:
1 1 1
1 2 3
the rows are in R^3
@paper egret p sure Hx = 0 will always be consistent becuase the augmented column will always be 0 in RREF
why not?
I probably have hang on
that's a pretty big theorem to skip
I know for sure I didn't skip anything
you need n linearly independent vectors to span a vector space with dimension n
that's a theorem
the first mention of "linearly independent" is ~15 pages down
but that makes sense though, to some degree
since if you have
1 2
2 4
or any two rows that are colinear
yeah
ah the "missing a row part" also makes sense now
its like you can't cover all of 2d space with only <1, 2>
yes
anyone who knows
I mean depends on how you want to define origin
if you define origin as the point that's (0,0,0,...0) (all coordinates 0)
then no
like what if you say span(v1,v2) is a line through the origin and v1
would that mean 0 vector and v1
also by zero vector do you mean "vector with 0 magnitude" or the vector with the zero vector property
um im not sure lol
I'll go with the latter
oh ok
since it says it's through the origin, it contains the origin too
doesn't mean the origin and zero vector would be the same
oh
so for my answer
i should just wrtie oigin
origin
origin + v1 or whatever
if it asked me to provide a geometric description of span(v1,v2) where they where v1 and v2 were independent
Define Vector space: $V=\left{\left(x_{1},x_{2}\right)| x_{1},x_{2}\in \mathbb{R} \right}$
Define vector addition: $\left( x_1,x_2 \right)+\left( y_1,y_2 \right) = (x_1+y_1+1, x_2+y_2+1)$
Define scalar multiplication: $c\left( x_1,x_2 \right) = \left( cx_1+c-1,cx_2+c-1 \right)$
the zero vector in this vector space is (-1,-1) if you want to verify it, so this is an example where the zero vector isn't the origin
PorosInMyAshe:
A geometric description would the plane that contains both vectors v1 and v2
that's what span(v1, v2) is
no one "finishes" lin alg
bad word choice
I'll never finish this book
Which is like a intro to linalg
ashe if you don't mind me asking how did you learn lin alg?
he breathed it probably
like most people in this server does.

then there's me and you doing it with sweat and blood
no pain; no gain
Does anyone know how to compute the basis of $W \cap V$ for subspaces W, V in R^5?
Notthebees:
Compile Error! Click the
reaction for details. (You may edit your message)
I forgot how to do any linear algebra computation, and my professor in my LAII course decided to give us a computation quesiton
how are your subspaces given
I learned introductory Lin alg in a university from a professor
all i did was write out c[L]b and b[L]c
and uh i have no idea where to go from there
theres this one too
i literally dont have a clue yikes
any insights would help โค
wait i misread nvm
In any general algebraic thingy, if Ax = A, then x is a "do nothing" element
Here, you have TST = T
ST is a "do nothing" which means S undoes T.
@scarlet hamlet
Mind you, this is probably not the only answer, but it's the easiest one to get
Oh wait, the kernel is non trivial so T isn't invertible. Huh.
I forgot some linear algebra, does the null space and range of a linear transformation change with respect to different basis?
@half ice i feel like im going in circles LOL
ive been writing stuff down
but its not leading anywhere
@scarlet hamlet do you know @cobalt tartan?
you two had the exact same question, in the exact same notation, one day apart lol
you can scroll up to see our discussion about the problem
Oh
(referring to your Q4)
๐
@scarlet hamlet q5 is uh you're almost definitely overthinking it
When u see it you'll go wtf did I spend 3 hours on this for
Q4 I'm still not 100% sure I understand lol
YEAH LOOOOOOOOOOOOL
What section
i knew id find another loo person here
uh
i have no idea
i have satriano at 1030
I thought UW meant University of Washington rip
Waterloo?
Cool cool. I was Western over here
Nice to see Canadian rep
You guys are the better school
Haha
my gpa is trash x.x
@scarlet hamlet Q5 uh what mapping takes T(x) and produces T(x)
Say that you have T:R^2-> R^2
Such that T((x, y)) = (y, x)
What is a linear mapping S:R2-> R^2
Such that S(T((x, y))) = (y, x)
@scarlet hamlet?
S(y,x) = (y,x)?
waterloo is too big brain for me
Same
Uh I'm low IQ wannabe mathie ๐
i lowkey celebrated when i did q1 and q2 on my own
did u already do it
whoa
i spent the whole weekend applying to jobs so im SoOo behind
Uh I looked at it, gonna start tonight
Have uh stat midterm to catch up + study for first
Ya
and they lived happily ever after
yeah
don't write matrices like what you just did. matrix entries don't need to be single-digit integers
There's no solution to your system, as the bottom line implies 0 = 3
@north sierra
by using the definition of linear independence
you use different coefficients for each, but yeah
Do what would i do from here?
How do i set up the matrix for this?
Is it correct to say c_2 must be 0 since its the only one that contains x^2
Trichloromethane:
Compile Error! Click the
reaction for details. (You may edit your message)
Is my proof also enough to show that span(u,v,w) = span(u,v)?
pog I saw university of waterloo in chat
@steady fiber r u a fellow looser too
no
I'm at the much better university a few hundred kilometers away
but every single one of my high school friend is at waterloo
lol uoft?
u of tears
sorry but we won the meme war
<@&286206848099549185>
like 95% of the time its people ponging helpers randomly and the other 5% its reposti
can my proof generalize for span(u,v,w) = span(u,v)
Do you think you can
oh wait
i proved span(u,v) is a subset of span(u,v,w)
so by the definition of the subset
No, you proved what you said you did
i mean earlier
in another part of the problem
my bad
but why cant i say span(u,v,w) = span(u,v) for my proof?
You showed that any element in span(u,v,w) is also an element in span(u,v). This proves that span(u,v,w) is a subset of span(u,v)
With that recent picture
oh yeah, because if i think of it in terms of the logical form of the subset
an element of span(u,v,w) -> span(u,v)
i mean also just intuitively
It's a very easy proof to show that span(u,v) is a subset of span(u,v,w)
yeah
So you should be able to show they're equal sets
BamBam:
Assuming X is not invertible because the case when it evaluates to I is obvious
So $X X^\dagger$ is a projection but projection onto what
BamBam:
Oh
Thanks
I just realized it is explained in wiki but i mustve scrolled past it earlier
is this correct?
im not sure if i can multiply c_1,c_2,c_3 by the different derivatives
you can because it's true for all x
yall this is a dumb question
if im asked to find 3(AB)
i find AB then scale it by 3 right ...
you could do that, sure
Iโm not asking for homework help but I came across this problem that I need help with
โProve L(x)=(uโขv)v is a linear transformationโ
Any insight?
are you sure you didn't mistype anything
as written it either doesn't make any sense or is underspecified
post the whole question exactly as it is stated
@dusky epoch hey sorry I donโt have access to the question anymore
R^3โ>R^3 mapping
Are you sure it's not something like "Show that L(x)=(x \cdot v)v is a linear transformation for any fixed v in R^3."?
Can someone guide me through to find the special solution to
1
Sorry about the pic
you mean, a particular solution?
Just gaussian elimination it
WAIT
erm
It has no solution lol
because you can't multiply a 3x4 matrix with a 3x1 matrix
does it say anywhere that it can't be [1, 1] and [2x, y]
but it's better to split the coefficients and variables
and I believe it's necessary for normal form
it gives some useful properties (as outlined in the description you linked)
What specifically is meant by this?
Are ui and vit supposed to be full matrixes in of themselves?
they're vectors
$(I+AB)$ is invertible, prove that $(I+BA)$ is invertible too
Nguyแป n Thร nh Trung:
hm...any idea
my proof is
if lambda is an eigenvalue of AB then it is also an eigenvalue of BA
so det(I+AB) = (1+lambda_1)(1+lambda_2)...(1+lambda_n) = det(I+BA)
I hope to see another idea for this problem :p
my gut instinct when I see something of this form is to use a geometric series if possible
$(I-X)^{-1} = I+X+X^2+X^3+\cdots$
Merosity:
just make sure things converge
well, idk if that's the best approach here, I think making use of this is a good idea:
$B(I+AB) = B+BAB = (I+BA)B$
Merosity:
there should be the same kind of relation for A as well, just some general ideas to think about that might help
BA and AB have the same eigenvalues except for a bunch of zeros (in this case, they're square matrices of equal dimensions, so they have the exact same eigenvalues with the same algebraic multiplicities). Since (I+AB) is invertible, -1 isn't an eigenvalue of AB, so it's not an eigenvalue of BA either. If -1 isn't an eigenvalue of BA, (I+BA) must be maximal rank, and therefore must be invertible
Probably
Is there a restriction on using eigenvalues
This is probably the simplest way to prove it
yeah
but this problem was on a midterm exam
eigenvalue hadn't have introduced
so I'm looking for another method
Oh there is another way
Find an explicit expression for the inverse of I+AB in terms of I, A, B and BA
It will automatically prove the existence of the inverse of I+BA
assume the inverse of (I + BA) is (I + C)
for some C
then find what C can be
this is important for that
Nguyแป n Thร nh Trung:
you mixed up the order of A and B
huh?
we want to find the inverse of I + BA not I + AB
we already have the inverse of I + AB
Nguyแป n Thร nh Trung:
$(I + BA) C = -BA$
Sigma:
Nguyแป n Thร nh Trung:
$C = -(I+C)BA = -BA - CBA$
Nguyแป n Thร nh Trung:
@@
let $C = (I+AB)^{-1}$\
$(I+BA)BCA = BCA + BABCA = B(C+ABC)A = B((I+AB)C)A=BIA=BA$\
$(I+BA)(BCA-I)=(I+BA)BCA - (I+BA) = BA - I - BA = -I$\
Nguyแป n Thร nh Trung:
done
hey quick nub question here
to compute the norm of a vector v in an inner product space
does that work for all of 2x2 matrices in R?
ok ignore i asked that
i wrote out a general one and figured it out myself :p
i'm pretty confused by this question and what its asking
what does it mean when it says 'set of prices when the price for output is 100 units'?
i've gotten to this stage but when i row reduced i got the 1 1 1 matrix which isn't useful
so i think i made a mistake somewhere
guys why a linear transformation is injective if and only if the kernel is 0 ??
I need some help to understand it conceptually, geometrically
this is how i think
do you understand injective => zero kernel?
derivatives can be represented as matrix operators right?
Kind of. They'd have to be infinite if your vector space is all polynomials
And we don't like to call them matrices anymore if they're infinite in size
But if you're limiting the degree, which is still a vector space, then yes definitely
this is a good explanation for the linear transformation 0 kernel thing https://math.stackexchange.com/a/2193375
the main thing is understanding what linearity means and then it's clear
@rancid sonnet
Calculus on Manifolds, by Michael Spivak (Chapter 2) might give you more information on what you want
about derivatives being linear operators (and being represented by matrices)
there's a lot more to it, so check out the book if you'd like (and don't skip Chapter 1)
ok I will
But if I represent polynomials with column matrices, then can I represent complex polynomials with a matrix too, by adding a second column for the imaginary components?
you don't need to add an extra column
you mean just make the elements complex?
yeah, complex numbers are still a field so they're not really any different in terms of polynomials
I want to solve some eigenvalue problems by trying to represent the hamiltonian operator of quantum mechanics as a matrix by writing some python code, so since I can write derivatives and polynomials as matrices, I'm gonna give it a try
I mean complex numbers are different, and you can't assume complex valued polynomials are differential everywhere (or even, anywhere)
yeah but in terms of just defining a polynomial by coefficients it makes more sense to just keep it as one column vector
wait how do I represent polynomial multipllication in matrices?
I got my second derivative operator in the hamiltonian
but if I want to express the potential as an operator
I should express it as an nxn matrix instead of a nx1 polynomial matrix right?
Should I just
enter coefficients of x^0 to x^(n-1) in the first row and
cycle them
in the next rows?
"yeah, complex numbers are still a field so they're not really any different in terms of polynomials"
laughs in Algebraically closed
laughs in ordered field
is the only way to get an identity matrix
by multiply a matrix & the inverse of that matrix
RokettoJanpu:
what if AB = Identity
wuld that mean B is the inverse of A
or A is the inv of B
like would it mean both of those statements*
RokettoJanpu:
$A^{-1}A = AA^{-1}=I$
so if AB = I, then A=IB^-1 correct?
couldnt you make it A=B^-1 since I is like 1
wait
AB = I, so B is the inverse of A
that's assuming both B is a square matrix
yes
A,B,C are all nxn
so is it possible for AC to = 0 without C = 0? ๐ค
i dnt think so bc if A is invertible, then AC=0 -> AA^-1C=0 -> C = 0
but maybe bc u dont have to put A^-1
but then if u solve for C u would just get 0
try A = [[1, 0], [0, 0]] and C = [[0, 0], [0, 1]]
see
how yall come up w matrices like that that fast ๐ญ ๐ญ
so it is possible for AC to = 0 if C = that matrix u put
for AC = 0, the image of C has to be in the null space of A
obviously, if C = 0, then the image of C is just the 0 vector and that condition is trivially satisfied
I find that most of the time when people say that they haven't learned it, they really have, but just didn't understand it.
question
if y = mx+b and m (slope) is undefined does that mean it's equal to 0?
or is a 0 slope undefined??
I'm given two points with an undefined slope so how do i figure the slope intercept form
when y-y1 = m(x-x1)
0 slope is not undefined slope
so how can i solve the point slope formula if i have a undefined slope
slope intercept*
@hardy blaze if AB=I then that means B is the inverse of A if and only if A is the inverse of B. Also yes you can have two matricies not equal to the zero matrix that multiply to be zero. You can show this for a 2X2 case easily as someone else has shown you. Just multpily any arbitrary matrices 2X2 and then find what conditions will satisfy it to be the zero matrix. Linear is abstract but you'll get it eventually
@formal moss 0 slope is not the same as undefined slope. Think of it as rise over run. 0 slope can be written as (0/n) for any number n so we rise 0 and move right any amount. undefined slope is kinda like (n/0) so we rise any amount and move right zero. however this is kinda over simplifying it since you cannot divide by zero that is why it is undefined
so the points are (2/3 , 1/5) and m is undefined and to find the answer in slope int form the answer is 2/3 and idk why
if you have an undefined slope which coordinate will stay the same on your line?
@formal moss
b?
oh
the x coordinate will
hmm i dont think thats right either
its the y
coordinate
that stays the same
yes! so usually we have the equation y=mx+b but what you just said was our x will always be 2/3. so instead of y= youll have an equation x=
do you know what the equation x= will be?
umm
and you were right first with the x stays the same for an undefined slope
perfect!
OOH
i see
2/3rds is our constant and with an undefined slope(rise over run) it's just x = 2/3?
you got it
oddly enough that sounds like the same as if it was 0 slope though
draw the line x=1 and y=1. what do you notice? they are similar but have a big difference
o
yes. x=1 is vertical and y=1 is horizontal
oh
ok
so it's an undefined function where the constant is 2/3rds
if a slope is undefined?
its not even a function actually
since x=2/3 fails the VLT
yes. x=2/3 is not a function
no problem
Can anyone help me with a problem
Let V = the x1x3 plane (also called xz plane) in R3, and v1 = (1,0,2), v2 = (0,0,1). Is V contained in Space {v1,v2}?
@sand oak
You mean Span{v1, v2}? Yes, it is. In fact, they're the same space
What are complex numbers used for? Searching online gets me stuff like "used for electricity" but no examples.
lots of things. circuit analysis is certainly one application
Can you give an example of how complex numbers are used to solve problems
quantum mechanics too but that's for the physics discord server
wait I'm dumb, complex variables is literally right under this channel
also physics discord? gib
Circuit analysis isn't a small one lol.
You can't get into any real integral transformations without including complex numbers
Differential equations will include complex at some point, and differential equations are a lot of engineering
did i say it was a small one?
No, you didn't
I'm still ignorant of how to apply them so I can't really "imagine" a scenario using them. Can you give a problem which would require them
Know what a derivative is?
I think so
you have a circuit which consists of a resistor, inductor, and capacitor strung together in series with an alternating voltage source (it drives AC current). you can use complex numbers to model the current through the circuit over time (look up phasors too)
A differential equation is an equation that relates an equation to it's derivatives. Lots of natural phenomena follow differential equations. Getting information out of them isn't always easy, but integral transforms are a common way to do it.
The Fourier transform needs complex numbers, even if the system you're looking at is real
Huh, interesting
I mean you can do the "easy" fourier transform with R->R^2
discussion belongs in #real-complex-analysis tbh
which is how it's often first taught
sadly
where you do cosine and sine seperately
What Roketto and I are describing are related, a transform allows one to find phasors
I think anyway? I know you can use the Laplace to get circuit stuff
Np, feel free to ask if you have any questions about it
@toxic pendant so one quick thing you can notice for complex numbers is the formula for the roots of a cubic. now for specific cubics youll naturally run into imaginary numbers using the formula. but after computing it youll get a real solutions (all the imaginary parts cancel each other) and you end up with a real solution. however you go through a "world" of a number system that real numbers cant explain in order to get your solution. so imaginary numbers can arise to get real solutions. in the lift of a plane to model some stuff imaginary numbers will naturally arise having a real meaning in how the system behaves
Thank you for the explanation
you may or may not have saved me from the wikipedia rabbit hole
I can show you some stuff if you have some time
I find it nice to take a geometric perspective and see the complex numbers as extending the number line into a 2D number plane
gives a very clear interpretation of exponents and multiplication, just ping me if you're around
will do
Hey yall I got a quick question
I'm sure this is actually super simple but idk where to start
Or what to get from this
,rotate -90
do you have access to determinants
Nope that's all I was given, or were you asking if I can find them from this system of eq?
I guess you have to get your hands dirty and do gaussian elimination then
@upbeat niche i'm not asking whether or not you were given anything else, i'm asking whether your class has ever talked about determinants in any capacity
So in my lin alg class I have this,
When they say that "If and only if v = 0", say for example that we have some p in P^2
p(x) = ax^2 + bx + c
Right?
When they say that the vector has to be the 0 vector do they mean that we have to have p(x) = 0x^2 + 0x + 0?
Or does p(x) = 0, i.e, the values of its roots...?
the first statement
the actual "value" of x doesn't matter when talking about the vector space P^2
hrm
think of them as just letter placeholders, they're basis vectors taking on no value
I'm a bit confused about this question then,
Oke
As I can like... find p, q, and values of a, b, c such taht it's not positive definite...?
here a,b,c are like separate x values, they're not the coefficients on the polynomials
Hrm
it's like they've taken the original version but they've repeated it three times in a row
Ok so like is the question basically saying
Let p(x) = p_1x^2 + p_2x + p_3, q(x) = q_1x^2 + q_2x + q_3, and then go and show that for any a, b, c, the inner product defined in the question will equal 0 if and only if p_1 = p_2 = p_3 = 0 or q_1 = q_2 = q_3 = 0?
very close, it's not wrong exactly, but
the last part I think should be said as when p=q and then p_1 = p_2 = p_3 = 0
since they're not separate
I think I might be nitpicking in a confusing way
yeah
Err because positive definite is'nt on different p and q, it's on the same p and q
Like
Do I just expand it out...?
Like I can do that but it's a lot of work lol
well, I'd just rewrite it with p=q and then
all the terms are really just (p(a))^2 + (p(b))^2+ (p(c))^2
Ya
yes exactly
is it clear how this is gonna work out, you don't have to expand anything
p(a) is just a number
it could be positive or negative
p(a)^2 however is always positive
Yea, <p, p> = 0 iff p(a)^2 = p(b)^2 = p(c)^2
But because it's in p2
You can only have 2 roots
So it's impossible for all 3 to be 0
not necessarily you also have to have p(a)^2 = p(b)^2 = p(c)^2=0
Err yea
Only in the case where it's all 0
Oh wait
Hrm
Well, showing positive/negative is easy enough, p(a), p(b), p(c) are all real numbers, when you square them , they will always be positive, so their sum will also be positive
Right?
yeah good
Hrmmm yea my roots idea would have like 3 cases which is too much work
Would need to show the case where 3 distinct, 2 distinct, and no distinct
And my idea only holds for 3 distinct/no distinct
it's good cause you have been given all 3 are distinct
I don't think I follow your reasoning, I'm imagining 3 quadratics each having at most 2 roots
Well because it's <p, p> we only have one quadratic
err, 1 quadratic
One quadratic would have at most 2 roots
3 results of plugging in 3 roots to 1 quadratic which can have at most 2 roots
Ah wait no
so one of them can't be 0

