Proving Basis of Dual Space: V* in P_n(F)

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In summary: If (a_0f_0+...+a_nf_n)(p(x)) = 0 then a_0=...=a_n=0.In summary, the book suggests that in order to find a basis for a vector space, you need to find a set of vectors that are linearly independent and that span the space. The hint suggests that you can do this by finding a set of scalars that are the roots of a polynomial. First you need to define the polynomial and then you need to find a set of scalars that equals it.
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jclawson709
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Homework Statement



Let V = P_n(F), and let c_0, c_1,..., c_n be distinct scalars in F. For 0 <= i <= n, define f_i(p(x)) = p(c_i). Prove that {f_0, f_1,..., f_n} is a basis for V*. Hint: Apply any linear combination of this set that equals the zero transformation to p(x) = (x-c_1)*(x-c_2)*...*(x-c_n), and deduce that the first coefficient is zero.

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The Attempt at a Solution



I really have no clue where to start on this one. First off, I don't even see how doing what the hint says would prove it to be a basis. Is the hint implying that these scalars are the roots of the polynomial? Also I'm not sure how I would pick a linear combination of the set that equals 0. I was thinking of using c_i - a*c_j, where a is just another scalar which causes a*c_j = c_i, so that it equals zero and the resulting transformation equals zero. Not too sure about that reasoning though, and how it would play into the hint the book gives.
 
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OK, let's do this thing. Don't think about the hint just yet. First, we'll start of with some definitions. What exactly is a basis of a vector space? This will be what we'll try to prove...
 
  • #3
ok so yeah, a basis is a set of vectors that are linearly independent. so if {f_0,...,f_n} is a basis then a_0f_0 + a_1f_1 + ... + a_nf_n = 0 if and only if a_0, a_1, ..., a_n are all zero, right?
 
  • #4
jclawson709 said:
ok so yeah, a basis is a set of vectors that are linearly independent.

Hmm, that's a start but it's not completely true. A basis is certainly a set of linearly independent vectors, BUT there's another very important requirement: you will want the vectors to span the space! But let's not deal with spanning the space just now, let's focus on the linearly independent part:

so if {f_0,...,f_n} is a basis then a_0f_0 + a_1f_1 + ... + a_nf_n = 0 if and only if a_0, a_1, ..., a_n are all zero, right?

Right, so you take [tex]a_0,...,a_n[/tex] such that [tex]a_0f_0+...a_nf_n=0[/tex]. Our aim is to show that [tex]a_0=...=a_n=0[/tex].

This is where the hint comes in, in order to show that [tex]a_0=...=a_n=0[/tex], you'll want to define the polynomial [tex]p(x)=(x-c_1)...(x-c_n)[/tex]. Now, calculate what [tex](a_0f_0+...+a_nf_n)(p(x))[/tex] equals.
 

Related to Proving Basis of Dual Space: V* in P_n(F)

1. What is the definition of a dual space?

A dual space is the set of all linear functionals on a vector space. In other words, it is the space of all linear maps from the vector space to its underlying field, typically denoted as V*.

2. How is the basis of the dual space V* related to the basis of the original vector space?

The basis of the dual space V* is composed of the dual vectors, which are linear functionals that map each basis vector of the original vector space to its corresponding component in the field. Therefore, the basis of V* is the set of linear functionals that are dual to the basis vectors of the original vector space.

3. How do you prove that a set of vectors is a basis for the dual space V*?

To prove that a set of vectors is a basis for the dual space V*, you need to show that every dual vector in V* can be written as a unique linear combination of the basis vectors. This can be done by showing that the basis vectors span V* and are linearly independent.

4. Are there any specific properties that a basis for the dual space V* must have?

Yes, there are two main properties that a basis for the dual space V* must have. First, the basis vectors must be linearly independent. Second, the number of basis vectors must be equal to the dimension of the original vector space.

5. How is the dual space V* useful in linear algebra?

The dual space V* is useful in linear algebra because it allows for the representation of linear transformations as matrices, making computations and calculations easier. It also provides a way to generalize concepts such as inner products and orthogonality to vector spaces that do not have a traditional notion of distance or angle.

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