Linear Algebra: Analyzing A Linear Transformation

In summary, the columns of matrix D can be determined by using any two of the other vertices of the parallelogram, but not all three. Using the origin and vertices A, B, and C, along with their corresponding images A', B', and C', the first column of D is (2,2) and the second column is (6,4). However, if the columns are swapped, the image of B will have coordinates (10,8), which is incorrect.
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Zoey93
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Hey,

I need help with part D2. My explanation is not right so I honestly do not know what I am suppose to write. My assignment is attached to this thread.

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  • #2
The columns of $D$ can be determined by using any two of the other vertices of the parallelogram.
It would be wrong to choose $(8,6)$ as a column, so not all of the remaining three vertices can serve as columns. Let $O$ be the origin, $A(1,0)$, $B(1,1)$ and $C(0,1)$. Let also $A'(2,2)$, $B'(8,6)$ and $C'(6,4)$. Then $D$ should map $\overrightarrow{OA}$ to $\overrightarrow{OA'}$ and $\overrightarrow{OC}$ to $\overrightarrow{OC'}$. Then $\overrightarrow{OB}=\overrightarrow{OA}+\overrightarrow{OC}$ will automatically be mapped to $\overrightarrow{OA'}+\overrightarrow{OC'}=\overrightarrow{OB'}$. Alternatively, $\overrightarrow{OA}$ can be mapped to $\overrightarrow{OC'}$ and $\overrightarrow{OC}$ to $\overrightarrow{OA'}$; the location of $B'$ will be the same. In the first scenario
\[
D\begin{pmatrix}1\\0\end{pmatrix}=\begin{pmatrix}2\\2\end{pmatrix},
\]
so the first column of $D$ is $(2,2)$. Also
\[
D\begin{pmatrix}0\\1\end{pmatrix}=\begin{pmatrix}6\\4\end{pmatrix},
\]
so the second column of $D$ is $(6,4)$. In the second scenario the columns are swapped.

But if, say, $D=\begin{pmatrix}2&8\\2&6\end{pmatrix}$, then $D$ maps $\overrightarrow{OA}$ to $\overrightarrow{OA'}$ and $\overrightarrow{OC}$ to $\overrightarrow{OB'}$. In this case, $\overrightarrow{OB}=\overrightarrow{OA}+\overrightarrow{OC}$ will be mapped to $\overrightarrow{OA'}+\overrightarrow{OB'}$, and the coordinates of the resulting vector, i.e., the coordinates of the image of $B$, are $(10,8)$, which is incorrect.
 

FAQ: Linear Algebra: Analyzing A Linear Transformation

What is linear algebra and why is it important?

Linear algebra is a branch of mathematics that deals with the study of linear transformations and their properties. It involves the use of matrices, vectors, and linear equations to represent and solve various problems. Linear algebra is important because it has a wide range of applications in fields such as physics, engineering, computer science, and economics.

What is a linear transformation?

A linear transformation is a function that maps one vector space to another in a way that preserves the algebraic structure. It can be represented by a matrix and has properties such as linearity, homogeneity, and preservation of the zero vector. In simpler terms, a linear transformation is a mathematical operation that transforms one set of data into another set of data while maintaining certain rules.

What are the different methods used to analyze a linear transformation?

There are several methods that can be used to analyze a linear transformation, including matrix representation, eigenvalues and eigenvectors, determinants, and rank. Matrix representation involves representing the linear transformation with a matrix and performing operations on it to analyze its properties. Eigenvalues and eigenvectors are used to determine how the transformation affects the direction and magnitude of vectors. Determinants are used to determine if the transformation changes the area or volume of the vector space. And rank is used to determine the dimension of the transformed vector space.

How is linear algebra used in data analysis and machine learning?

Linear algebra plays a crucial role in data analysis and machine learning. It is used to represent and manipulate data in the form of matrices and vectors, making it easier to perform operations and calculations. Linear algebra is also used in algorithms for tasks such as data compression, dimensionality reduction, and pattern recognition. In machine learning, linear algebra is used to build and train models that can make predictions and decisions based on data.

Can linear algebra be applied in real-life situations?

Yes, linear algebra has many real-life applications, such as in engineering for designing structures and solving electrical circuits, in physics for analyzing motion and forces, in economics for studying supply and demand, and in computer graphics for creating 3D images and animations. It is also used in everyday tasks such as optimizing routes for transportation and analyzing financial data. Understanding linear algebra can help in solving real-world problems more efficiently and accurately.

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