Exploring Linear Transformations on Basis Elements of P3(R)

In summary, the conversation discusses the relevance of applying a linear transformation to the basis elements of P3(R) and how this subset helps determine the range of the transformation. It is mentioned that the basis elements of P3(R) are 1, x, x^2, and x^3, and their corresponding basis elements in R4 are T(1), T(x), T(x^2), and T(x^3). The output of the transformation on any element in P3(R) can be written as a linear combination of these basis elements in R4.
  • #1
Butelle
12
0
Hi

I am trying to do a math assignment and I am finding it really difficult.

Assume you have a linear transformation from T: P3(R) --> R4

What relevance is there to applying the transformation to the basis elements of P3(R), ie: T(1), T(x), T(x^2), T(x^3)? Why is this subset special? How does it help determine the range of T?

Thanks.
 
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  • #2
[itex]dim(P^3)=dim(\mahtbb{R}^4)=4[/itex]

you have the basis elements of [itex]P^3[/itex].

The action of T on each of these basis elements will let you know the basis elements of [itex]\mathbb{R}^4[/itex]

i.e. [itex]1,x,x^2,x^3[/itex] are the basis elements of [itex]P^3[/itex]
and [itex]T(1),T(x),T(X^2),T(x^3)[/itex] are the basis elemetns of [itex]\mathbb{R}^4[/itex]

applying T to any element of [itex]p(x) \in P^3[/itex] will yield [itex]T(p(x)) \in \mathbb{R}^4[/itex] and [itex]T(p(x))=aT(1)+bT(x)+cT(x^2)+dT(x^3)[/itex] where [itex]a,b,c,d \in \mathbb{Z}[/itex]
 
  • #3
thank you!
 

Related to Exploring Linear Transformations on Basis Elements of P3(R)

1. What is a linear transformation?

A linear transformation is a function that maps one vector space to another while preserving the basic structure of the original space. This means that the transformation must follow two rules: scaling and addition. In other words, multiplying a vector by a scalar and adding two vectors must result in the same value before and after the transformation.

2. How is a linear transformation represented?

A linear transformation can be represented in several ways, but the most common is through a matrix. The columns of the matrix represent the images of the basis vectors of the original space, and the linear combination of these images represents the transformation of any given vector in the space. Another way to represent a linear transformation is through a set of equations, called the transformation equations, which describe how each coordinate of the input vector is transformed into a coordinate of the output vector.

3. What are the properties of a linear transformation?

There are a few key properties that a linear transformation must have. These include:

  • Addition Preservation: The transformation must preserve vector addition. In other words, if two vectors are added in the original space, their images under the transformation must also be added.
  • Scalar Multiplication Preservation: The transformation must preserve scalar multiplication. This means that multiplying a vector by a scalar in the original space must result in the same scalar being multiplied to its image under the transformation.
  • Preservation of the Zero Vector: The transformation must map the zero vector in the original space to the zero vector in the output space.

4. What is the difference between a linear transformation and a non-linear transformation?

The main difference between a linear transformation and a non-linear transformation is that a linear transformation must follow the rules of scaling and addition, while a non-linear transformation does not have to. In other words, a linear transformation can only map a vector space to another vector space, while a non-linear transformation can map a vector space to a different type of space, such as a curve or a surface.

5. How are linear transformations used in real life?

Linear transformations have numerous real-life applications, especially in fields such as physics, engineering, and computer graphics. For example, in physics, linear transformations are used to describe the motion and forces acting on objects, while in computer graphics, they are used to create 3D models and animations. Linear transformations are also used in data analysis and machine learning, where they are used to transform and manipulate data to extract meaningful information.

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