(n-1)-dimensional subspace is the null space of a linear functional

In summary, given that N is an (n-1)-dimensional subspace of an n-dimensional vector space V, a linear functional can be defined such that N is its null space. This is achieved by selecting a basis for N and extending it to a basis for V, and defining the linear functional to be zero for the basis vectors of N and arbitrary nonzero values for the other basis vectors.
  • #1
yifli
70
0
Given that N is an (n-1)-dimensional subspace of an n-dimensional vector space V, show that N is the null space of a linear functional.

My thoughts:

suppose [tex]\alpha_i[/tex]([tex]1\leq i \leq n-1[/tex]) is the basis of N, the linear functional in question has to satisfy f([tex]\alpha_i[/tex])=0.

Am I correct?

Thanks
 
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  • #2
Yes, that is already correct!
 
  • #3
Given a basis for any vector space, any linear functional depends only upon its "action" on those basis vectors- that, is given f(v1)= x1, f(v2)= x2, ..., f(vn)= xn where the v's are the basis vectors and the a's are scalars, then any vector v can be written as v= a1v1+ a2v2+ ...+ anvn for some scalars, a1, a2, ..., an. Since f is linear, f(v)= a1f(v1)+ a2f(v2)+ ...+ anf(vn)=a1x1+ a2x2+ ... anxn.

Given any subspace U of vector space V, select a basis {u1, u2,...,um} for U and extend it to a basis for V. Define f(u1)= f(u2)= ...= f(um)= 0 and define f to be whatever nonzero values you like for the other basis vectors. Then the nullspace of f is exactly U.
 

1. What does it mean for a subspace to be the null space of a linear functional?

A subspace is the null space of a linear functional if all vectors within the subspace are mapped to 0 by the linear functional. In other words, the subspace contains all vectors that satisfy the equation f(x) = 0, where f is the linear functional and x is a vector in the subspace.

2. Can a subspace have more than one linear functional that maps it to 0?

Yes, it is possible for a subspace to have multiple linear functionals that map it to 0. This is because there can be multiple ways to define a linear functional that satisfies the equation f(x) = 0 for all vectors in the subspace. However, all of these linear functionals will have the same null space.

3. How is the dimension of a subspace related to the dimension of its null space?

The dimension of a subspace is equal to the dimension of its null space, also known as its nullity. This is because the null space contains all vectors that are mapped to 0 by the linear functional, and these vectors form a basis for the subspace.

4. Can the null space of a linear functional be the entire vector space?

Yes, it is possible for the null space of a linear functional to be the entire vector space. This means that the linear functional maps all vectors in the vector space to 0, making it a trivial linear functional. In this case, the null space would have the same dimension as the vector space.

5. How can we use the concept of null space of a linear functional in real-world applications?

The concept of null space of a linear functional is commonly used in linear algebra and functional analysis to solve systems of linear equations and to study vector spaces and linear transformations. It has various applications in fields such as engineering, physics, and computer science, including image and signal processing, data compression, and machine learning.

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