Eigenvalues and eigenvectors for linear transformation

In summary: The Attempt at a Solution(a) To find all functions such that T(f)=f'=mf, I separate it into 2 cases, 1st is when m=0when m=0, all constant function will form the eigenspace of T.2nd, m is all real number except 0I can only think of 1 type of function that satisfy f'=mf, which is when f(x)=ae^(bx+c) +d for all real a,b,c,d where a,b=/0 which corresponds to eigenvalue m=ab However, it seems very not convincing and there seems to be a better
  • #1
Lily@pie
109
0

Homework Statement


V is a vector space consisting all functions f:R->R that is differentiable many times

(a) Let T:V->V be the transformation T(f)=f'
Find the (real) eigenvectors and eigenvalues of T

(b) Let T be transformation T(f)=f"
Prove that all real number, m is the eigenvalue of T

Homework Equations


For part a, it means to describe the eigenspace of T for each eigenvalue m

The Attempt at a Solution


(a) This means that to find all functions such that T(f)=f'=mf
I separate it into 2 cases, 1st is when m=0
when m=0, all constant function will form the eigenspace of T.
2nd, m is all real number except 0
I can only think of 1 type of function that satisfy f'=mf
which is when f(x)=ae^(bx+c) +d for all real a,b,c,d where a,b=/0 which corresponds to eigenvalue m=ab
However, it seems very not convincing and there seems to be a better way of writing this.

(b) This means that we need to show there exist a eigenspace for all m.
I separated into 3 cases
1st, m=0. All functions of the form f(x)=ax+b for all real a,b where a=/0 is the eigenvector.
2nd, m=-ve. All functions of the form f(x)=a sin(x)+b cos(x) for all non zero a,b.
3rd, m=+ve. I'm clueless ... T-T
 
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  • #2
Ad b): The second derivative of f(x) = a sin(x)+b cos(x) is -f(x), so that's just an eigenfunction for eigenvalue -1.

Also, you might want to have a look at ex.

Just out of curiosity, have you learned about complex numbers yet?
 
  • #3
So should I put f(x) = ca sin(x)+cb cos(x) instead of f(x) = a sin(x)+b cos(x)?

For f(x)=ex, isn't it f(x)=f'(x)=f"(x) so it'll work for eigenvalue 1? So if I let f(x)=aex, can I conclude that all non zero real number is the eigenvalue for T?

Ya, I've learned complex numbers but the question actually state only get the real eigenvectors and eigenvalues.
 
  • #4
(b) This means that we need to show there exist a eigenspace for all m.
I separated into 3 cases
1st, m=0. All functions of the form f(x)=ax+b for all real a,b where a=/0 is the eigenvector.
No, you don't want that restriction on a. a= 0, that is f(x)= b, has the property that f'(x)= 0 which is an eigenvector with m= 0.

2nd, m=-ve. All functions of the form f(x)=a sin(x)+b cos(x) for all non zero a,b.
Where did you get this? The second derivative of f(x)= a sin(x)+ b cos(x) is f''(x)= -a sin(x)- b cos(x) which is of the form f''= mf only if m= -1.
More generally, the solutions to f''= mf, for m negative, are of the form [itex]f(x)= a sin(\sqrt{m} x)+ b cos(\sqrt{m} x)[/itex].

3rd, m=+ve. I'm clueless ... T-T
Try [itex]f(x)= ae^{\sqrt{m}x}+ be^{-\sqrt{m}x}[/itex]
equivalently, [itex]f(x)= a sinh(\sqrt{m}x)+ b cosh(\sqrt{m}x)[/itex].
 
  • #5
Lily@pie said:
For f(x)=ex, isn't it f(x)=f'(x)=f"(x) so it'll work for eigenvalue 1? So if I let f(x)=aex, can I conclude that all non zero real number is the eigenvalue for T?
What is the second derivative of f(x) = a ex? Can you get it equal to m f(x) for any number m?

Lily@pie said:
Ya, I've learned complex numbers but the question actually state only get the real eigenvectors and eigenvalues.

True, but there is an elegant way to solve the problem using complex numbers. You just need to slightly modify the example I gave you. Think about how you can get any number in front when you differentiate ex (the position where HallsOfIvy placed the [itex]\sqrt{m}[/itex] should give you a pretty good hint).
 
  • #6
HallsofIvy said:
More generally, the solutions to f''= mf, for m negative, are of the form [itex]f(x)= a sin(\sqrt{m} x)+ b cos(\sqrt{m} x)[/itex].

Try [itex]f(x)= ae^{\sqrt{m}x}+ be^{-\sqrt{m}x}[/itex]
equivalently, [itex]f(x)= a sinh(\sqrt{m}x)+ b cosh(\sqrt{m}x)[/itex].

Hmm, I know it works but how did you get this 2 equations just by looking at the question? I understand the part where it relates to sin, cos and sinh, cosh... But just the term inside the bracket... How did you derive it?
 
  • #7
Have you not taken elementary differential equations? The "linear differential equation with constant coefficients", f''= mf, has "characteristic equation" r^2= m which has roots [itex]r= \pm\sqrt{m}[/itex] if m is positive and [itex]r= \pm i\sqrt{m}[/itex] if m is negative. The negative roots give [itex]f= Ce^{i\sqrt{-m}x}+ De^{i\sqrt{-m}x}= C'cos(\sqrt{-m}x)+ D'sin(\sqrt{-m}x)[/itex] while positive roots [itex]f= Ce^{\sqrt{m}x}+ De^{-\sqrt{m}x}[/itex].

If you did not know that, how did you get the sine and cosine solution?
 
  • #8
CompuChip said:
What is the second derivative of f(x) = a ex? Can you get it equal to m f(x) for any number m?

So if we let f(x)=aemx, then we will get f"(x)=am2emx=m2f(x); which is what we want.
So, is it true that we can conclude f(x)=emx will do coz a is not important?

CompuChip said:
True, but there is an elegant way to solve the problem using complex numbers. You just need to slightly modify the example I gave you. Think about how you can get any number in front when you differentiate ex (the position where HallsOfIvy placed the [itex]\sqrt{m}[/itex] should give you a pretty good hint).

Erm, I don't really understand but I'd tried the following but I can't reason...

let f(x)=a sin(nx) + b cos(nx)
so f"(x) = -n2f(x)
Can we derive from here as n can be all real number and n2 is always positive, m will always be a negative value when f(x)=a sin(nx) + b cos(nx). But how does the term [itex]\sqrt{m}[/itex] comes into place?
 
  • #9
HallsofIvy said:
Have you not taken elementary differential equations? The "linear differential equation with constant coefficients", f''= mf, has "characteristic equation" r^2= m which has roots [itex]r= \pm\sqrt{m}[/itex] if m is positive and [itex]r= \pm i\sqrt{m}[/itex] if m is negative. The negative roots give [itex]f= Ce^{i\sqrt{-m}x}+ De^{i\sqrt{-m}x}= C'cos(\sqrt{-m}x)+ D'sin(\sqrt{-m}x)[/itex] while positive roots [itex]f= Ce^{\sqrt{m}x}+ De^{-\sqrt{m}x}[/itex].

If you did not know that, how did you get the sine and cosine solution?

I got the sine and cosine solution by thinking that that is the way a function will get back itself whenever we differentiate it twice.

I have learned difference equation for discrete calculus. But does it work the same way as I can understand the part of the characteristic equation to get roots [itex]r= \pm\sqrt{m}[/itex]... But, still a bit lost in the later part on getting the function related to ex...
 
  • #10
Lily@pie said:
So if we let f(x)=aemx, then we will get f"(x)=am2emx=m2f(x); which is what we want.
So, is it true that we can conclude f(x)=emx will do coz a is not important?

Yep. If you think about eigenvectors for matrices, then it is also true that if v is an eigenvector, then so is r v for any real number r (not equal to zero). The same holds here: if em x is an "eigenvector" (although we usually call them eigenfunctions in the continuous case) then so is r em x for any real number r (not equal to zero).

Note that actually there are two solutions, and the eigenspace is spanned by these: just like with the sines and cosines the general eigenvector looks like f(x) = a em x + ...

As for the "elegant" solution I was hinting at: you have shown that f''(x) = m2 f(x). So if you only consider m2 > 0 you have shown that all the positive numbers are eigenvalues. But m2 can also be negative. In that case m must be imaginary and you will get a solutions of the form f(x) = a ei n x (if m = i n where n is a real number). If you again write down the general form and use Euler's identity, you can rewrite this to the solution with the sines and cosines you already had.
 
  • #11
Lily@pie said:
So if we let f(x)=aemx, then we will get f"(x)=am2emx=m2f(x); which is what we want.
So, is it true that we can conclude f(x)=emx will do coz a is not important?
Yes, the coefficent "a" is not important because if [itex]\vec{v}[/itex] is an eigenvector of a given linear transformation, then so is any multiple of [itex]\vec{v}[/itex].



Erm, I don't really understand but I'd tried the following but I can't reason...

let f(x)=a sin(nx) + b cos(nx)
so f"(x) = -n2f(x)
Can we derive from here as n can be all real number and n2 is always positive, m will always be a negative value when f(x)=a sin(nx) + b cos(nx). But how does the term [itex]\sqrt{m}[/itex] comes into place?
The original equation was f''= -m f,. not f''= -n2. [itex]m= n^2[/itex].

And, if [itex]f(x)= ae^{nx}+ be^{-nx}[/itex] then [itex]f'(x)= ane^{nx}- bne^{-nx}[/itex] and [itex]f''(x)= an^2e^{nx}+ bn^2e^{-nx}= n^2(ae^{nx}+ be^{-nx})= n^2f(x)[/itex].
 

1. What are eigenvalues and eigenvectors?

Eigenvalues and eigenvectors are concepts in linear algebra that are used to describe the behavior of linear transformations. Eigenvalues represent the scaling factor of a linear transformation, while eigenvectors represent the direction in which the transformation acts.

2. How do you find eigenvalues and eigenvectors?

To find eigenvalues and eigenvectors for a linear transformation, you need to solve the characteristic equation of the transformation's corresponding matrix. This is done by subtracting the identity matrix multiplied by a scalar from the transformation's matrix, and then finding the values of the scalar that make the determinant of the resulting matrix equal to zero. The eigenvectors can then be found by plugging the eigenvalues back into the original equation.

3. What is the significance of eigenvalues and eigenvectors?

Eigenvalues and eigenvectors have many practical applications in fields such as physics, engineering, and computer science. They are used to describe the behavior of systems that involve linear transformations, such as vibrations, rotations, and electrical circuits. They are also essential in data analysis and machine learning, where they are used to reduce the dimensionality of data and identify patterns.

4. Can a linear transformation have multiple eigenvalues and eigenvectors?

Yes, a linear transformation can have multiple eigenvalues and eigenvectors. In fact, most linear transformations have an infinite number of eigenvalues and eigenvectors. However, some transformations may have repeated eigenvalues or linearly dependent eigenvectors, which means that they do not provide additional information.

5. How are eigenvalues and eigenvectors related to diagonalization?

Eigenvalues and eigenvectors are closely related to diagonalization, which is the process of finding a diagonal matrix that is similar to the original transformation's matrix. Diagonalization is only possible if the transformation has a complete set of linearly independent eigenvectors. This is because the eigenvectors form the basis for the new coordinate system in which the transformation can be represented as a diagonal matrix.

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