Linear transformation, basis of the image

In summary, the conversation discusses the relationship between polynomial functions and the map I, which is a linear transformation. It is shown that this map is injective and a basis for its image is determined. The conversation also mentions using polynomial functions to represent this map in matrix form.
  • #1
Chris1557
2
0

Homework Statement


From Calculus we know that, for any polynomial function f : R -> R of degree <= n, the function
I(f) : R -> R, s -> ∫0s f(u) du, is a polynomial function of degree <= n + 1.

Show that the map
I : Pn -> Pn+1; f -> I(f),
is an injective linear transformation, determine a basis of the image of I and fi nd the matrix
M in M(n+2)x(n+1)(R) that represents I with respect to the basis 1,t,...,tn of Pn and the basis 1,t,...,tn+1 of Pn+1.

Homework Equations



The Attempt at a Solution


I found a topic involving setting f(x) = anxn + an-1xn-1 + an-2xn-2 + ... + a1x + a0, and g(x) = bnxn + bn-1xn-1 + bn-2xn-2 + ... + b1x + b0.
Then show that L(f + g) = L(f) + L(g) and that aL(f) = L(ax).

That seems ok, but I have no idea how to determine a basis of the image and I am confused on what the final part in notating and what to do.

Thanks.
 
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  • #2
Chris1557 said:
I found a topic involving setting f(x) = anxn + an-1xn-1 + an-2xn-2 + ... + a1x + a0, and g(x) = bnxn + bn-1xn-1 + bn-2xn-2 + ... + b1x + b0.
Then show that L(f + g) = L(f) + L(g) and that aL(f) = L(ax).

That's a very good start. This will show that we are dealing with a linear transformation.

That seems ok, but I have no idea how to determine a basis of the image and I am confused on what the final part in notating and what to do.

Let's first determine injectivity. For this, we take a basis of Pn (can you find an easy basis for this?). Take the image of this basis under I and determine whether this image is linear dependent. If so, the function is injective, and behold: the image of the basis is (in that case) a basis for the image!
 
  • #3
micromass said:
Let's first determine injectivity. For this, we take a basis of Pn (can you find an easy basis for this?). Take the image of this basis under I and determine whether this image is linear dependent. If so, the function is injective, and behold: the image of the basis is (in that case) a basis for the image!

For the easy basis of Pn can we just use xn, xn-1,..., x, 1

How do we take the image of the basis under I?

The matrix part is also confusing me.
 
  • #4
Just calculate I(xn),...,I(x),I(1). This should be an easy calculation...
 

Related to Linear transformation, basis of the image

What is a linear transformation?

A linear transformation is a mathematical function that maps one vector space to another in a way that preserves the basic structure of the vector space. It is a fundamental concept in linear algebra and is used to represent various types of operations, such as rotations, reflections, and dilations.

What is the basis of an image?

The basis of an image, also known as the standard basis, is a set of vectors that can be used to represent any vector in a vector space. It is a fundamental concept in linear algebra and is used to describe the coordinates of a vector in terms of its component vectors.

How do you determine the basis of an image?

To determine the basis of an image, you need to find a set of linearly independent vectors that span the vector space. This can be done by solving a system of linear equations, finding the null space of a matrix, or using other techniques such as the Gram-Schmidt process.

What is the purpose of finding the basis of an image?

The purpose of finding the basis of an image is to simplify the representation of vectors and linear transformations in a vector space. By using a basis, we can reduce the number of variables needed to describe a vector or a linear transformation and make calculations easier and more efficient.

How is the basis of an image related to linear independence?

The basis of an image is a set of linearly independent vectors. This means that each vector in the basis is unique and cannot be expressed as a linear combination of the other vectors. Linear independence is important because it allows us to describe a vector space in the most concise and efficient way.

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