Linear algebra, find a basis for the quotient space

In summary, the authors choose the basis {1+W, x+W} to solve the problem because they have an infinite number of linearly independent polynomials that satisfy p(1)=p(-1)=0.
  • #1
Karl Karlsson
104
12
Homework Statement
Let V = C[x] be the vector space of all polynomials in x with complex coefficients and let ##W = \{p(x) ∈ V: p (1) = p (−1) = 0\}##.

Determine a basis for V/W
Relevant Equations
V = C[x]
##W = \{p(x) ∈ V: p (1) = p (−1) = 0\}##.
Let V = C[x] be the vector space of all polynomials in x with complex coefficients and let ##W = \{p(x) ∈ V: p (1) = p (−1) = 0\}##.

Determine a basis for V/W

The solution of this problem that i found did the following:
sol.png

Why do they choose the basis to be {1+W, x + W} at the end? I mean since
##dim(ker(L)) + dim (im(L)) = dim(V), dim(ker(L)) = dim(W)## and then ##dim(im(L))=2 = dim(V) - dim(W)## that means ##dim(W) \rightarrow \infty## because there is an infinite number of linearly independent polynomials that satisfy p(1)=p(-1)=0. Can't I just choose W to be a basis for V/W?

Thanks in advance!
 

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  • #2
I think there's some confusion. W in the vector space V/W is a single vector, and is the zero vector. Recall V/W is the space of sets of the form x+W for some x, where is x-y is in W then x+W = y + W.

This is what they mean when they write 1+W and x+W.

If you really want the whole set W to be your sweet of basis (which doesn't even work, since they don't even span the space in question) you would have many linearly dependent vectors and you wouldn't have a basis.

As you observed some of the vector spaces have infinite dimensions, so your formula for the dimension of the image and kernel don't actually make sense. I can't read what is written in the solution but it looks like they take a different approach to show V/W has 2 dimensions
 
  • #3
##p(1)=0## means ##(x-1)\,|\,p(x)## and ##p(-1)=0## gives ##(x+1)\,|\,p(x)##, so we have ##(x^2-1)\,|\,p(x)##. Now ##V/W = \mathbb{C}[x]/(x^2-1)\cdot\mathbb{C}[x]##, which means we can reduce any polynomial ##p(x)## to one of degree at most ##1## by writing ##q(x)=f(x)\cdot (x^2-1)+ g(x)\, , \,\deg g \leq 1.## Thus we have ##\pi(p)=g## if ##\pi : V\longrightarrow V/W## is the canonical projection. Since ##g(x)## is of degree zero or one, we can use ##[ g]=1## and ##[g]=x## as representatives of the equivalence classes. You cannot use dimension formulas, as ##\infty## isn't a number.
 
  • #4
Alternatively, since [itex]x^{2k} - 1[/itex] and [itex]x^{2k+1} - x[/itex] vanish at [itex]x = \pm 1[/itex] and are non-zero if [itex]k \geq 1[/itex] one can write [tex]
\begin{align*}
\sum_{n \geq 0} a_nx^n &= a_0 + a_1 x + \sum_{k \geq 1} a_{2k}x^{2k} + \sum_{k \geq 1} a_{2k+1} x^{2k+1} \\
&= \left(a_0 + \sum_{k \geq 1} a_{2k}\right) + x\left( a_1 + \sum_{k \geq 1} a_{2k+1}\right)
+ \sum_{k \geq 1} a_{2k} (x^{2k} - 1) + \sum_{k \geq 1} a_{2k+1} (x^{2k + 1} - x)\end{align*}[/tex] which is a polynomial of degree at most 1 plus a polynomial in [itex]W[/itex].
 
  • #5
Office_Shredder said:
I think there's some confusion. W in the vector space V/W is a single vector, and is the zero vector. Recall V/W is the space of sets of the form x+W for some x, where is x-y is in W then x+W = y + W.

This is what they mean when they write 1+W and x+W.

If you really want the whole set W to be your sweet of basis (which doesn't even work, since they don't even span the space in question) you would have many linearly dependent vectors and you wouldn't have a basis.

As you observed some of the vector spaces have infinite dimensions, so your formula for the dimension of the image and kernel don't actually make sense. I can't read what is written in the solution but it looks like they take a different approach to show V/W has 2 dimensions
pasmith said:
Alternatively, since [itex]x^{2k} - 1[/itex] and [itex]x^{2k+1} - x[/itex] vanish at [itex]x = \pm 1[/itex] and are non-zero if [itex]k \geq 1[/itex] one can write [tex]
\begin{align*}
\sum_{n \geq 0} a_nx^n &= a_0 + a_1 x + \sum_{k \geq 1} a_{2k}x^{2k} + \sum_{k \geq 1} a_{2k+1} x^{2k+1} \\
&= \left(a_0 + \sum_{k \geq 1} a_{2k}\right) + x\left( a_1 + \sum_{k \geq 1} a_{2k+1}\right)
+ \sum_{k \geq 1} a_{2k} (x^{2k} - 1) + \sum_{k \geq 1} a_{2k+1} (x^{2k + 1} - x)\end{align*}[/tex] which is a polynomial of degree at most 1 plus a polynomial in [itex]W[/itex].
Hi Office_Shredder! Thanks that is just the type of solution i was looking for :)
 
  • #6
fresh_42 said:
##p(1)=0## means ##(x-1)\,|\,p(x)## and ##p(-1)=0## gives ##(x+1)\,|\,p(x)##, so we have ##(x^2-1)\,|\,p(x)##. Now ##V/W = \mathbb{C}[x]/(x^2-1)\cdot\mathbb{C}[x]##, which means we can reduce any polynomial ##p(x)## to one of degree at most ##1## by writing ##q(x)=f(x)\cdot (x^2-1)+ g(x)\, , \,\deg g \leq 1.## Thus we have ##\pi(p)=g## if ##\pi : V\longrightarrow V/W## is the canonical projection. Since ##g(x)## is of degree zero or one, we can use ##[ g]=1## and ##[g]=x## as representatives of the equivalence classes. You cannot use dimension formulas, as ##\infty## isn't a number.
Hi fresh_42! Thanks for another great solution that was easy to follow!
 
  • #7
Hi just for clarification. Does the basis ##\{1+W, x + W\}## for V/W refer to all multiples of x+W and 1+W meaning for example 2x + 7+ W is also within the basis above?
 
  • #8
Karl Karlsson said:
Hi just for clarification. Does the basis ##\{1+W, x + W\}## for V/W refer to all multiples of x+W and 1+W meaning for example 2x + 7+ W is also within the basis above?
[itex]2x + 7 + W[/itex] is not a basis vector. It is within the space spanned by the basis, since [itex]2x + 7 + W = 2(x + W) + 7(1 + W)[/itex] since if [itex]p \in W[/itex] then [itex]9p \in W[/itex].
 
  • #9
Karl Karlsson said:
Hi just for clarification. Does the basis ##\{1+W, x + W\}## for V/W refer to all multiples of x+W and 1+W meaning for example 2x + 7+ W is also within the basis above?
All linear combinations: ##\mathbb{C}\cdot (1+W) + \mathbb{C}\cdot (x+W)##.
 
  • #10
The answer ##\{1+W,x+W\}## to this problem {W+1,W+x} spans all of V, right? If this problem instead would have been the exact same but V was the vector space of all polynomials of degree n (n+1 vectors). Wouldn't the solution have been exactly the same? And then ##dim(V/W) = dim(V)-dim(W) = 2##, but clearly the basis ##V/W = \{1+W,x+W\}## has more then just two linearly independent vectors since W contains n-1 linearly independent vectors, right?
 
  • #11
Karl Karlsson said:
The answer ##\{1+W,x+W\}## to this problem {W+1,W+x} spans all of V, right?
No. It spans ##V/W## not ##V=\mathbb{C}[x].##
If this problem instead would have been the exact same but V was the vector space of all polynomials of degree n (n+1 vectors). Wouldn't the solution have been exactly the same?
Yes, the resulting quotient would be the same, i.e. an isomorphic copy to be exact, namely isomorphic to all linear polynomials.
And then ##dim(V/W) = dim(V)-dim(W) = 2##, but clearly the basis ##V/W = \{1+W,x+W\}## has more then just two linearly independent vectors since W contains n-1 linearly independent vectors, right?
No. ##W## is the zero vector in ##V/W##. There is no resolution of ##W## into elements of ##V## anymore.

Test the concept with ##V=\mathbb{Z}## and ##W= 12\mathbb{Z}## instead. It isn't a vector space, only a ring and an ideal, but it helps to understand quotient building. What are the generators of ##V/W = \mathbb{Z}/12\cdot\mathbb{Z}## and how can you represent them most conveniently?
 
Last edited:

1. What is linear algebra?

Linear algebra is a branch of mathematics that deals with the study of vector spaces and linear transformations. It involves the use of algebraic methods to solve systems of linear equations and to study the properties of geometric objects such as lines, planes, and higher-dimensional analogues.

2. What is a quotient space?

A quotient space, also known as a factor space, is a vector space that is obtained by dividing a given vector space by a subspace. This is done by identifying all vectors in the subspace as equivalent and considering them as a single vector in the quotient space. The quotient space allows us to study the properties of a vector space in a more simplified manner.

3. What is a basis for a quotient space?

A basis for a quotient space is a set of vectors that span the quotient space and are linearly independent. This means that any vector in the quotient space can be expressed as a linear combination of the basis vectors. A basis for a quotient space is also known as a set of coset representatives.

4. How do you find a basis for a quotient space?

To find a basis for a quotient space, we first need to find a basis for the subspace that we are dividing by. Then, we can use the basis for the subspace to construct a basis for the quotient space. This can be done by taking any set of vectors that span the quotient space and applying the method of Gaussian elimination to reduce them to a set of linearly independent vectors.

5. Why is finding a basis for a quotient space important?

Finding a basis for a quotient space is important because it allows us to study the properties of a vector space in a more simplified manner. It also helps us to understand the relationship between the quotient space and the subspace that we are dividing by. Additionally, a basis for a quotient space can be used to find solutions to linear systems of equations and to perform other operations in linear algebra.

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