Linear transformation of matrices

In summary: T(\textbf{u}_3) = T(1,0,1)^T = (2, -1)^T \neq 0v_1 + -v_2In summary, we found the matrix of the linear transformation T with respect to the bases B and B' to be \begin{pmatrix} 1 & 0 & 0 \\ 0 & 0 & -1 \end{pmatrix}. However, when applying this transformation to the basis vectors, we see that they do not result in the expected values. This could be due to a mistake in the calculation or an inconsistency in the problem itself. Further analysis is needed to determine the cause of
  • #1
geft
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Sorry for the poor formatting, I keep getting errors with Latex in preview. I'll appreciate anyone who could reformat the question into Latex.

Let T: R3 -> R2 be defined by T(x, y, z)T = (x +z, y - z)T. Find the matrix of T with respect to the bases B = {(1,1,0)T,(0,1,1)T,(1,0,1)T} and B' = {(1,1)T,(-1,1)T}.

Here is what I've done. I don't know what's wrong and I'm going crazy because I can't find what's wrong with it. I believe it's my careless mistake, but I've been checking again and again to no avail.

T(1,0,0)T = (1, 0)T
T(0,1,0)T = (0, 1)T
T(0,0,1)T = (0, -1)T

AT = (1 0 0, 0 1 -1)T
PB = (1 0 1, 1 1 0, 0 1 1)T
PB' = (1 -1, 1 1)T

PB'-1 = 1/2 (1 1, -1 1)T

PB'-1ATPB = (1 0 0, 0 0 -1)

T(u1) = T(1,1,0)T = (1, 1)T [tex]\neq[/tex] v1 + 0v2
T(u2) = T(0,1,1)T = (1, 0)T [tex]\neq[/tex] 0v1 + 0v2
T(u3) = T(1,0,1)T = (2, -1)T [tex]\neq[/tex] 0v1 + -v2
 
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  • #2
Let T: \mathbb{R}^3 \rightarrow \mathbb{R}^2 be defined by T(\textbf{x}) = (x_1 + x_3, x_2 - x_3)^T. Find the matrix of T with respect to the bases B = \{(1,1,0)^T,(0,1,1)^T,(1,0,1)^T\} and B' = \{(1,1)^T,(-1,1)^T\}.

Here is what I've done. I don't know what's wrong and I'm going crazy because I can't find what's wrong with it. I believe it's my careless mistake, but I've been checking again and again to no avail.

T(\textbf{e}_1) = T(1,0,0)^T = (1, 0)^T
T(\textbf{e}_2) = T(0,1,0)^T = (0, 1)^T
T(\textbf{e}_3) = T(0,0,1)^T = (0, -1)^T

A_T = \begin{pmatrix} 1 & 0 & 0 \\ 0 & 1 & -1 \end{pmatrix}
P_B = \begin{pmatrix} 1 & 0 & 1 \\ 1 & 1 & 0 \\ 0 & 1 & 1 \end{pmatrix}
P_{B'} = \begin{pmatrix} 1 & -1 \\ 1 & 1 \end{pmatrix}

P_{B'}^{-1} = \frac{1}{2} \begin{pmatrix} 1 & 1 \\ -1 & 1 \end{pmatrix}

P_{B'}^{-1}A_TP_B = \begin{pmatrix} 1 & 0 & 0 \\ 0 & 0 & -1 \end{pmatrix}

T(\textbf{u}_1) = T(1,1,0)^T = (2, 1)^T \neq v_1 + 0v_2
T(\textbf{u}_2) = T(0,1,1)^T = (1, 0)^T \neq 0v_1
 

FAQ: Linear transformation of matrices

What is a linear transformation of matrices?

A linear transformation of matrices is a mathematical operation that maps one set of values to another set of values while preserving their linear relationship. It is often used to transform data or solve systems of linear equations.

How is a linear transformation of matrices represented?

A linear transformation of matrices is represented by a matrix multiplication. The matrix on the left represents the transformation, while the matrix on the right represents the original data.

What are the properties of a linear transformation of matrices?

The properties of a linear transformation of matrices include:

  • Preservation of linearity: the transformation preserves the linear relationship between the original data and the transformed data.
  • Preservation of origin: the transformation maps the origin of the original data to the origin of the transformed data.
  • Preservation of addition: the transformation preserves addition of vectors.
  • Preservation of scalar multiplication: the transformation preserves scalar multiplication of vectors.

How is a linear transformation of matrices applied in real-world situations?

Linear transformations of matrices are used in many real-world situations, such as:

  • Data compression: transforming a large dataset into a lower-dimensional representation while preserving the relationship between data points.
  • Image processing: transforming images through rotations, scaling, and other operations.
  • Robotics: transforming coordinates and movements of robots in a 3D space.
  • Financial modeling: transforming financial data to analyze trends and make predictions.

What is the difference between a linear transformation of matrices and a nonlinear transformation?

A linear transformation of matrices preserves the linear relationship between data points, while a nonlinear transformation does not. This means that a nonlinear transformation can change the shape of the data, while a linear transformation can only stretch, rotate, or reflect it. Additionally, a linear transformation can be represented by a matrix multiplication, while a nonlinear transformation cannot.

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