Matrices & Geometric Transformations

In summary, the conversation discusses the use of determinants to find the area of a transformed figure. The most direct approach is to apply the transformation matrices to the vectors and then find the area of the resulting figure. There are also theorems that relate the determinants of the transformation matrices to the area. The determinant of a transformation matrix is used to calculate the scale factor of the image and thus the area of the transformed figure. The conversation also clarifies that the determinant should be applied to the vector (1, 0) and (0, 1) to find the area.
  • #1
synkk
216
0
nG1oA.png


Part c) I'm not quite sure what to do, I've found the det(U) is 2, but no idea what this actually shows to be honest, any help?
 
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  • #2


What do you mean by det(U)? U isn't a matrix. It's a square.
 
  • #3


The most direct thing to do is apply the two matrices to each of the vectors (0, 0), (1, 0), (0, 1), and (1, 1) to determine the new figure. Then find the area of that. There are also theorems relating the determinants of the transformation matrices to the area.
 
  • #4


HallsofIvy said:
The most direct thing to do is apply the two matrices to each of the vectors (0, 0), (1, 0), (0, 1), and (1, 1) to determine the new figure. Then find the area of that. There are also theorems relating the determinants of the transformation matrices to the area.

Thank you, I've done that and got the right answer however in the solutions it simply states determinant of RS = 2, therefore area scale factor of U is 2, therefore image of U has area 2. I don't understand what the determinant has to do with it, could you explain please?


vela said:
What do you mean by det(U)? U isn't a matrix. It's a square.
I mean det(RS), my apologies, thanks.
 
  • #5
Then clearly the author of your text expects you to know that the area of a two dimensional object, when it transformed by A, is multiplied by det(A).

If [tex]A= \begin{bmatrix}a & b \\ c & d\end{bmatrix}[/tex] is applied to the vector (1, 0), we have, of course, (a, c) and if it is applied to the vector (0, 1), we have (b, d). That is, the square having (1, 0) and (0, 1) as sides (and so area 1), is transformed into the parallelogram having (a, c) and (b, d) as sides. And, of course, the area of that parallelogram is the length of the cross product of those two vectors (taking [itex]\vec{k}[/itex] to be 0 to make it three dimensional). That cross product is
[tex]\left|\begin{array}{ccc}\vec{i} & \vec{j} & \vec{k} \\ a & c & 0 \\ b & d & 0 \end{array}\right|= (ad- bc)\vec{k}[/tex] and has length ad- bc, the determinant of A.
 

1. What are matrices and how are they used in geometric transformations?

Matrices are rectangular arrays of numbers that are used to represent linear transformations. In the context of geometric transformations, matrices are used to describe the transformations of points, lines, and shapes in 2D or 3D space. Each entry in the matrix represents a specific transformation, such as translation, rotation, or scaling.

2. What is the difference between a 2D and 3D matrix in geometric transformations?

A 2D matrix is a 3x3 array of numbers, while a 3D matrix is a 4x4 array of numbers. The extra row and column in a 3D matrix allows for translations in addition to rotations and scalings, which are possible with a 2D matrix. 3D matrices are used for transformations in 3D space, while 2D matrices are used for transformations in 2D space.

3. How do I perform a geometric transformation using matrices?

To perform a geometric transformation using matrices, you first need to represent the transformation as a matrix. Then, you can multiply the matrix by the coordinates of the points, lines, or shapes you want to transform. The resulting matrix will give you the new coordinates of the transformed object.

4. Can matrices be used for non-linear transformations?

No, matrices are only used for linear transformations. Non-linear transformations, such as bending or twisting, require more complex mathematical tools such as calculus.

5. How are matrices and geometric transformations used in real-world applications?

Matrices and geometric transformations are used in many real-world applications, including computer graphics, image processing, and robotics. They are also used in physics and engineering for modeling and simulating complex systems. Additionally, matrices are used in statistics and data analysis for data transformation and manipulation.

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