The Matrix Of A Linear Transformation

In summary, the conversation discusses how to prove that T is an isomorphism by finding Mbb(T) when B = {1, x, x^2, ... , x^n}. The process is similar to finding Mdb(T). The matrix Mbb(T) is given as [1 0 0 ... 0; 0 2 0 ... 0; 0 0 3 ... 0; 0 0 0 ... (n+1)], which is invertible. This means that T is both one-to-one and onto, making it an isomorphism. The conversation also mentions a theorem from the textbook that states that a linear transformation is onto if and only if the rank of the
  • #1
playboy
I saw a similar post to this one, but i just got lost in the mess of the whole thing. So i just started a new thread.

A question reads:

Let T: Pn ---> Pn be defined by T[P(x)] = p(x) + xp'(x), where p'(x) denotes the derivative. Show that T is an isomorphism by finding Mbb(T) when B = {1, x, x^2, ... , x^n}

From doing the other question and problems in the textbook, i know how to find Mdb(T). I suppose that finding Mbb(T) would be very similar.

I did it like this:

Mbb(T) = [ CbT(1) CbT(x) CbT(x^2) ... CbT(X^n) ]
Mbb(T) = [ Cb(1) Cb(2x) Cb(3x^2) ... Cb((n+1)X^n]

and it gives this nxn matrix:

[1 0 0 ... 0]
[0 2 0 ... 0]
[0 0 3 ... 0]
[0 0 0 ... (n+1)]

now, an isomorphism means that the linear transformation is both one-to-one and onto.

How do you tell that its an isomorphism by just looking at the matrix?
 
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  • #2
In this case, your transformation is isomorphic if the matrix is invertible. Why?
 
  • #3
Their is a theorm in my textbook.

"Let A be an mXn matrix, and let TA : R^n ---> R^m be the linear transformation inudced by A, that is, Ta(X) = AX for all X in R^n.

1. Ta is onto if and only if rank A = m
2. Ta is one-to-one if and only if A = n

oh wait.. that dosn't work in this problem...

ill brb
 
  • #4
Their is an example in the textbook...

If U is any invertable mxm matrix, the map T: Mmn ----> Mmn given by T(X) = UX is an isomorphism by Example 6 Section 8.2

This example is too long to type out... but it dosn't show any matrix. It just uses the transformation to show it.
 

FAQ: The Matrix Of A Linear Transformation

What is a matrix of a linear transformation?

The matrix of a linear transformation is a representation of a linear transformation as a matrix. It is a way to describe the relationship between the input and output of a linear transformation using a matrix. This can help to simplify calculations and analysis of linear transformations.

How is the matrix of a linear transformation related to the standard matrix?

The standard matrix is a specific type of matrix that represents a linear transformation with respect to the standard basis. The matrix of a linear transformation is a more general representation that can be used for any basis. The standard matrix can be obtained from the matrix of a linear transformation by using a change of basis.

How can the matrix of a linear transformation be used to perform compositions and transformations?

The matrix of a linear transformation can be used to perform compositions and transformations by multiplying it with a vector representing the input. This results in a vector representing the output of the transformation. By combining multiple matrices of linear transformations, complex transformations can be easily performed.

What is the relationship between the determinant of the matrix of a linear transformation and the scaling factor of the transformation?

The determinant of the matrix of a linear transformation is equal to the scaling factor of the transformation. This means that the determinant can be used to determine the amount by which the transformation scales an object. A determinant of 1 indicates no scaling, while a determinant of 0 indicates that the transformation collapses the object to a single point.

Can the matrix of a linear transformation be used to solve systems of linear equations?

Yes, the matrix of a linear transformation can be used to solve systems of linear equations. This is because a system of linear equations can be represented as a matrix equation, where the matrix of the linear transformation represents the coefficients of the variables. By using techniques such as Gaussian elimination, the matrix can be manipulated to solve for the variables and obtain the solution to the system of equations.

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