Question about Matrix Linear Transformation

In summary, To find the standard matrix for a linear operator, put the matrix into reduced row echelon form and check if every row has a pivot. If it does, the transformation is one-to-one and onto. If not, it is neither. For the linear operator in the given example, the standard matrix is [-1 3 2; 2 0 4; 1 3 6]. To determine if it is one-to-one and/or onto, check if the determinant of the matrix is not 0. If it is an operator on R^3, it is injective and onto if the null space is trivial. The linear transformation is also one-to-one and onto if it has an inverse linear transformation
  • #1
albaboi123
5
0
i'm studying for my midterm and I'm stumped on this section about Lienar Transformations...hope u guys can help

Homework Statement



question goes something like this
1) Find the standard matrix for the linear operator define by the equations (which is easy)
and then determine wheter the operator is one-to-one and or onto

example question is
w1=-x+3y+2z
w2=2x+4z
w3=x+3y+6z

2) Show that the range of the linear operator defined by the equations
w1=4x-2y
w2=2x-y
is not all of R^2, and find a vector that's not in the range.




The Attempt at a Solution



1) so the standard matrix is

[-1 3 2]
[2 0 4]
[1 3 6]

now how do i tell if its 1-1 and/or onto?
 
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  • #2
Put the matrix into reduced row echelon form. If every row has a pivot, then the vectors are a basis for the codomain and the transformation is onto. The transformation is one-to-one if all the vectors are linearly independent. Since this is a 3x3 matrix, either the function will be both one-to-one and onto, or the transformation will be neither.
 
  • #3
A linear transformation is one-to-one and onto if and only if it has an inverse linear transformation. When it is written as a matrix, this is true if and only if the determinant of the matrix is not 0.
 
  • #4
Also, since it is an operator on R^3 it's injective and onto iff the null space is trivial

for a linear operator A the following are equivalent: A is invertible, A is injective, A is onto

since it is an operator you need only consider if the null space is trivial, since if it is trivial it is injective and hence bijective, if it is not then it is neither injective nor onto
 
Last edited:

Related to Question about Matrix Linear Transformation

1. What is a matrix linear transformation?

A matrix linear transformation is a mathematical operation that transforms a vector or set of vectors using a matrix. It involves multiplying the vector(s) by the matrix to create a new vector(s) with different coordinates. This transformation preserves properties such as linearity and can be used to perform operations such as rotations, scaling, and shearing.

2. How is a matrix linear transformation represented?

A matrix linear transformation is represented by a matrix, which is a rectangular array of numbers. The dimensions of the matrix depend on the number of input and output variables. For example, a 2x2 matrix can transform 2-dimensional vectors, while a 3x3 matrix can transform 3-dimensional vectors.

3. What is the difference between a matrix linear transformation and a general linear transformation?

A matrix linear transformation is a specific type of linear transformation that uses a matrix to perform the transformation. A general linear transformation can use other operations, such as rotations and reflections, in addition to matrix multiplication.

4. How is a matrix linear transformation applied in real-world applications?

Matrix linear transformations are commonly used in computer graphics, image processing, and physics simulations. They can also be applied in fields such as economics and engineering to model and analyze systems with multiple variables.

5. What are some properties of a matrix linear transformation?

Some properties of a matrix linear transformation include linearity, which means that the transformation preserves the structure of the original vector(s), and invertibility, which means that the transformation can be reversed. Matrix linear transformations also have a determinant and can be represented as a composition of simpler transformations.

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