Dual basis problem. (Linear Algebra)

In summary, the conversation is discussing how to prove that if m < n and if y_1,...,y_m are linear functionals on an n-dimensional vector space V, then there exists a non-zero vector x in V such that [x,y_j] = 0 for j = 1,..., m. The solution involves writing each functional in terms of its value at the basis vectors and using the properties of linear systems to find a non-trivial solution. It is also mentioned that this result shows that if m < n, there is a non-trivial solution to a system of linear equations with n unknowns and m equations.
  • #1
Gramsci
66
0

Homework Statement


Prove that if m < n and if y_1,...,y_m are linear functionals on an n-dimensional vector space V, then there exists a non-zero vector x in V such that [x,y_j] = 0 for j = 1,..., m


Homework Equations





The Attempt at a Solution


My thinking is somehow that we should write every functional in terms of its value at the basis vectors, but well, I'm not really sure what to do. Any help would be nice.
 
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  • #2
If e1, e2, ..., en are the basis vectors, we wish to find x = a1e1 + a2e2 + ... + anen such that

[tex]\left[
\begin{array}{c}
y_1(a_1e_1 + a_2e_2 + \ldots + a_ne_n) \\
y_2(a_1e_1 + a_2e_2 + \ldots + a_ne_n) \\
\vdots \\
y_m(a_1e_1 + a_2e_2 + \ldots + a_ne_n)
\end{array}\right] = \left[
\begin{array}{c}
0 \\ 0 \\ \vdots \\ 0
\end{array}\right]
[/tex]​

All I've done is rephrased the problem. From this, do you see how you would find a1, a2, ..., an?
 
  • #3
I've reached that point before, but from there I'm kinda stuck. My first thought was letting the first m entries of the vector be 0, but that wouldn't do anything. I thought something about representing each linear functional as a linear combination of the dual base vectors, but well, not completely sure how that'd help.
 
  • #4
Can you go from a basis of the dual space to find a corresponding basis in the original space?
 
  • #5
In continuation of my earlier advice, you will need to use the fact that each yj is a linear functional.
 
  • #6
Tedjn: I've used the properties to rewrite them into the form:
a_1y_1(e_1)+...+a_ny_1(e_n) = 0
etc. for all functionals. But still, nothing.
Office_Shredder:
I don't really understand what you're hinting at, sorry.
 
  • #7
Each yj(ei) is a constant element of the field. If V is a real vector space, then each yj(ei) is a constant real number. Does that format remind you of anything?
 
  • #8
Tedjn:
Linear systems! Then if each y_j(e_i) produces a constant real number, we have a system of linear equations in n unknowns with m equations, right?
 
  • #9
I believe that should work.
 
  • #10
Then we have to know that it has a non-trivial solution, right? Is there any way to know that besides reasoning that builds on "matrices"?
 
  • #11
And oh, there's a small follow up-question:
"What does this result say in general about the solutions of linear equations?"
I'd say that if we have n unknowns and m equations, there's a non-trivial solution, but that's just me.
 
  • #12
It may be possible; maybe you can approach it from a different angle by working with the dual basis as Office_Shredder suggested earlier. Personally, I prefer to think of this solution as one that builds on our knowledge of systems of linear equations, where matrices are just a form of bookkeeping for our coefficients.

EDIT: To the follow up, indeed it tells us that if m < n. But since we used that fact in the first place, it does make me wonder whether they were looking for a different solution.
 

Related to Dual basis problem. (Linear Algebra)

1. What is the dual basis problem in linear algebra?

The dual basis problem in linear algebra refers to finding the basis of the dual space that corresponds to a given basis of a vector space. This problem arises when working with dual spaces, which are the set of all linear functionals on a vector space. The dual basis allows us to define a unique representation for each element in the vector space.

2. Why is the dual basis problem important in linear algebra?

The dual basis problem is important because it helps us define and understand the dual space, which is a fundamental concept in linear algebra. The dual space allows us to work with linear functionals, which are essential for solving many problems in mathematics and physics. Additionally, the dual basis provides us with a useful tool for representing elements in a vector space in a unique way.

3. How is the dual basis problem solved?

The dual basis problem can be solved by using the Gram-Schmidt process, which is a method for finding an orthogonal basis from a given set of vectors. This process involves finding the orthogonal projection of each vector onto the subspace spanned by the previously found orthogonal vectors. The resulting set of vectors will form a basis for the dual space.

4. Can the dual basis problem be generalized to non-Euclidean vector spaces?

Yes, the dual basis problem can be generalized to non-Euclidean vector spaces. In fact, the dual space can be defined for any vector space over any field. However, in non-Euclidean spaces, the concept of orthogonality may not exist, so the Gram-Schmidt process cannot be used. Instead, other methods such as the QR decomposition can be used to find a basis for the dual space.

5. What are some applications of the dual basis problem?

The dual basis problem has many applications in mathematics and physics. In linear programming, the dual basis is used to find the dual solution to a given primal problem. In quantum mechanics, the dual basis is used to represent states and operators in the Hilbert space. In signal processing, the dual basis is used for frame theory, which is used for efficient signal representation and processing. Additionally, the dual basis has applications in optimization, control theory, and data compression.

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