Are Trigonometric Polynomials a Basis for a Complex Vector Space?

In summary: I believe so.Notice that the imaginary part of ##\lambda## will turn into your real part in the exponential, so the mod 2pi only needs to apply to the real part of ##\lambda## to prevent duplication in the imaginary part, since the real and imaginary parts of a complex number are always orthogonal.
  • #1
albega
75
0
I'm having trouble with a couple of things written in some notes I'm reading.

Firstly, in stating examples of vector spaces, they say
Trigonometric polynomials - Given n distinct (mod 2π) complex constants λ1,...,λn, the set of all linear combinations of enz forms an n-dimensional complex vector space.

Now does this even make sense? I feel as though it is trying to say that we can have enz for integer λn as the basis for the vector space of functions, i.e like Fourier series, but I'm not sure really.

Secondly, for the complex vector space defined by the vectors in the 2D plane
|r,φ>=(rcosφ,rsinφ)
where vector addition is as usual and scalar multiplication follows the rule
α|r,φ>=||α|r,φ+argα>
I'm having trouble proving that
α(|a>+|b>)=α|a>+α|b>
and
(α+β)|a>=α|a>+β|a>

For the second one, I do
(α+β)|a>=(α+β)|r,φ>
=||(α+β)|r,φ+arg(α+β)>
and I have no idea what to do next, as expanding the modulus/argument doesn't seem possible. Not sure about the second either.
 
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  • #2
albega said:
I'm having trouble with a couple of things written in some notes I'm reading.

Firstly, in stating examples of vector spaces, they say
Trigonometric polynomials - Given n distinct (mod 2π) complex constants λ1,...,λn, the set of all linear combinations of enz forms an n-dimensional complex vector space.

Now does this even make sense? I feel as though it is trying to say that we can have enz for integer λn as the basis for the vector space of functions, i.e like Fourier series, but I'm not sure really.
The λi's are complex, not integer.
albega said:
Secondly, for the complex vector space defined by the vectors in the 2D plane
|r,φ>=(rcosφ,rsinφ)
where vector addition is as usual and scalar multiplication follows the rule
α|r,φ>=||α|r,φ+argα>
I'm having trouble proving that
α(|a>+|b>)=α|a>+α|b>
and
(α+β)|a>=α|a>+β|a>
Show us where you've gotten on these. Start with the left side and expand it using the rule above for scalar multiplication.
albega said:
For the second one, I do
(α+β)|a>=(α+β)|r,φ>
=||(α+β)|r,φ+arg(α+β)>
and I have no idea what to do next, as expanding the modulus/argument doesn't seem possible. Not sure about the second either.
 
  • #3
For thinking of them as a basis set, first consider what it would take to normalize them.
Then, since the lambdas are distinct, mod 2pi, it can be shown that they are also orthogonal.
This is similar in concept to the Fourier basis, but with complex lambdas.
For the last part, you should be able to put it back into complex exponential form and demonstrate the properties you are looking for.
 
  • #4
Mark44 said:
The λi's are complex, not integer.
Show us where you've gotten on these. Start with the left side and expand it using the rule above for scalar multiplication.

Yeah, I know that's what it says, but I just don't understand how that is the case... A quick google of trigonmetric polynomials shows them to be the functions
sin(mx), cos(mx) or exp(imx) for integer m...

I've started off one of them at the bottom, but can't progress...
 
  • #5
RUber said:
For thinking of them as a basis set, first consider what it would take to normalize them.
Then, since the lambdas are distinct, mod 2pi, it can be shown that they are also orthogonal.
This is similar in concept to the Fourier basis, but with complex lambdas.
For the last part, you should be able to put it back into complex exponential form and demonstrate the properties you are looking for.

Hmm I think I'm having trouble understanding what it means by mod(2pi), especially with the lambdas being complex. Could you explain please?
 
  • #6
albega said:
Hmm I think I'm having trouble understanding what it means by mod(2pi), especially with the lambdas being complex. Could you explain please?
##\pi/6## and ##13\pi/6## are in the same congruence class, modulo ##2\pi##
 
  • #7
Mark44 said:
##\pi/6## and ##13\pi/6## are in the same congruence class, modulo ##2\pi##

Ok, so what happens if we have (13π/6)+2i, as we are allowed complex numbers? Is this (π/6)+2i?
 
  • #8
albega said:
Secondly, for the complex vector space defined by the vectors in the 2D plane
|r,φ>=(rcosφ,rsinφ)
where vector addition is as usual and scalar multiplication follows the rule
α|r,φ>=||α|r,φ+argα>
I'm having trouble proving that
α(|a>+|b>)=α|a>+α|b>
It might be easier to start with the right side, α|a>+α|b>.
You could write the vectors a and b as (r1cosφ1) and (r2cosφ2). According to the scalar multiplication rule above, what is αa? What is αb?
 
  • #9
albega said:
Ok, so what happens if we have (13π/6)+2i, as we are allowed complex numbers? Is this (π/6)+2i?
I believe so.
 
  • #10
Notice that the imaginary part of ##\lambda## will turn into your real part in the exponential, so the mod 2pi only needs to apply to the real part of ##\lambda## to prevent duplication in the imaginary part, since sin(x)=sin(x+2pi).
 
  • #11
RUber said:
Notice that the imaginary part of ##\lambda## will turn into your real part in the exponential, so the mod 2pi only needs to apply to the real part of ##\lambda## to prevent duplication in the imaginary part, since sin(x)=sin(x+2pi).

Ok, so are these actually trigonometric polynomials - wikipedia seems to claim they are just sin(mx), cos(mx), exp(imx) for integer m.

I'm confused about the whole mod(2pi) thing - why don't we just say we have all these lambdas and they must be between zero and 2pi?

So the vector space produced by linear combinations of these will be any function?
 
  • #12
Mark44 said:
It might be easier to start with the right side, α|a>+α|b>.
You could write the vectors a and b as (r1cosφ1) and (r2cosφ2). According to the scalar multiplication rule above, what is αa? What is αb?

αa=[(|α|r1)cos(φ1+argα),(|α|r1)sin(φ1+argα)],
αb=[(|α|r2)cos(φ2+argα),(|α|r2)sin(φ2+argα)]
I did try going down this route and things got very messy...

Anyway,
αa+αb=[(|α|r1)cos(φ1+argα)+(|α|r2)cos(φ2+argα),(|α|r1)sin(φ1+argα)+(|α|r2)sin(φ2+argα)]
 
  • #13
I'm having some trouble with your notation. Apparently, you're using this notation for a vector: |r, φ>.
albega said:
|r,φ>=(rcosφ,rsinφ)
where vector addition is as usual and scalar multiplication follows the rule
α|r,φ>=||α|r,φ+argα>
I'm having trouble proving that
α(|a>+|b>)=α|a>+α|b>
Here, you're writing vectors as |a>. Where is the angle?
 
  • #14
Mark44 said:
I'm having some trouble with your notation. Apparently, you're using this notation for a vector: |r, φ>.

Here, you're writing vectors as |a>. Where is the angle?

Well |a> is just the vector, but it has an associated modulus and argument so I can write |a>=|r,φ>.
 
  • #15
albega said:
αa=[(|α|r1)cos(φ1+argα),(|α|r1)sin(φ1+argα)],
αb=[(|α|r2)cos(φ2+argα),(|α|r2)sin(φ2+argα)]
I did try going down this route and things got very messy...

Anyway,
αa+αb=[(|α|r1)cos(φ1+argα)+(|α|r2)cos(φ2+argα),(|α|r1)sin(φ1+argα)+(|α|r2)sin(φ2+argα)]
This stuff would be easier to read if you put in some spaces. The expressions you have written don't have a single space anywhere.

The natural thing to do would be to use the sum formulas for sine and cosine, and see if you can get some simplification from doing that.
 
  • #16
albega said:
I'm confused about the whole mod(2pi) thing - why don't we just say we have all these lambdas and they must be between zero and 2pi?

So the vector space produced by linear combinations of these will be any function?

Since pi is irrational the the set of integers times x is equivalent to the set of distinct points in the range 0 to 2pi.
In any case, whether you use mx or lambda x, you end up with the same vector space.
 

Related to Are Trigonometric Polynomials a Basis for a Complex Vector Space?

1. What is a vector space?

A vector space is a mathematical structure that consists of a set of vectors and a set of operations that can be performed on those vectors. It follows certain axioms and properties, such as closure under addition and scalar multiplication, and having a zero vector and additive inverses.

2. What are the applications of vector spaces?

Vector spaces have a wide range of applications in mathematics, physics, engineering, and computer science. They are used to represent physical quantities such as forces and velocities, to model geometric transformations, and to solve systems of linear equations in linear algebra.

3. How do you determine if a set of vectors form a vector space?

To determine if a set of vectors form a vector space, we need to check if they satisfy all the axioms and properties of a vector space. This includes checking for closure under addition and scalar multiplication, existence of a zero vector and additive inverses, and satisfying the distributive and associative properties.

4. What is the dimension of a vector space?

The dimension of a vector space is the number of vectors in a basis for that space. It represents the minimum number of linearly independent vectors needed to span the entire space. For example, the dimension of a 3-dimensional space is 3, as it can be spanned by 3 linearly independent vectors.

5. What is the difference between a vector space and a subspace?

A vector space is a set of vectors that satisfy certain axioms and properties, while a subspace is a subset of a vector space that also satisfies those axioms and properties. In other words, a subspace is a smaller vector space contained within a larger vector space. All vector spaces have at least two subspaces - the zero subspace and the entire space itself.

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