Linear Algebra: Rotation Matrix Qθ+φ

In summary, the rotation by θ followed by a rotation by φ can be expressed as either two consecutive rotations or one rotation of (θ + φ). This can be shown by proving that Qθ Qφ = Qθ+φ, where Q is the rotation matrix. To do this, one can perform matrix multiplication and use the trig identities cos(\theta+ \phi)= cos(\theta)cos(\phi)- sin(\theta)sin(\phi) and sin(\theta+ \phi)= sin(\theta)cos(\phi)+ cos(\theta)sin(\phi).
  • #1
camchetan
1
0
Show that a rotation by θ followed by a rotation by φ can be expressed as either
two consecutive rotations, or one rotation of (θ + φ). That is, show that Qθ Qφ = Qθ+φ, where Q is the rotation matrix.

Can anyone answer this question I'm a beginner in Linear Algebra
 
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  • #2


What is the rotation matrix with angle [itex]\theta[/itex]??
 
  • #3
do the matrix multiplication and see what happens
 
  • #4
micromass said:
What is the rotation matrix with angle [itex]\theta[/itex]??

genericusrnme said:
do the matrix multiplication and see what happens
You will need the trig identities [itex]cos(\theta+ \phi)= cos(\theta)cos(\phi)- sin(\theta)sin(\phi)[/itex] and [itex]sin(\theta+ \phi)= sin(\theta)cos(\phi)+ cos(\theta)sin(\phi)[/itex]
 
  • #5


Sure, I can help you with this question. Let's start by defining the rotation matrix Qθ as follows:

Qθ = [cosθ -sinθ
sinθ cosθ]

Similarly, the rotation matrix Qφ can be defined as:

Qφ = [cosφ -sinφ
sinφ cosφ]

Now, let's consider the product Qθ Qφ, which represents a rotation by θ followed by a rotation by φ. We can express this product as follows:

Qθ Qφ = [cosθ -sinθ
sinθ cosθ] [cosφ -sinφ
sinφ cosφ]

Using matrix multiplication, we get:

Qθ Qφ = [cosθ cosφ - sinθ sinφ -cosθ sinφ -sinθ cosφ
sinθ cosφ + cosθ sinφ -sinθ sinφ + cosθ cosφ]

Simplifying this expression, we get:

Qθ Qφ = [cos(θ+φ) -sin(θ+φ)
sin(θ+φ) cos(θ+φ)]

This is exactly the rotation matrix for the rotation by (θ+φ). Therefore, we have shown that Qθ Qφ = Qθ+φ, which means that a rotation by θ followed by a rotation by φ can be expressed as one rotation of (θ+φ).

Alternatively, we can also express the product Qθ Qφ as two consecutive rotations. First, we rotate by θ and then by φ. This can be written as:

Qθ Qφ = [cosθ -sinθ
sinθ cosθ] [cosφ -sinφ
sinφ cosφ]

= [cosθ cosφ - sinθ sinφ -cosθ sinφ -sinθ cosφ]
[sinθ cosφ + cosθ sinφ -sinθ sinφ + cosθ cosφ]

= [cosθ cosφ - sinθ sinφ sinθ cosφ + cosθ sinφ
sinθ cosφ + cosθ sinφ -sinθ sinφ + cosθ cosφ]

= [cosθ cosφ - sinθ sinφ sinθ cosφ + cosθ sinφ
sinθ cosφ + cosθ sinφ -sinθ sinφ + cosθ cosφ]

= [cosθ cosφ - sinθ sinφ cos(π/2 - φ) + sin(π/2 - θ) sinφ
 

What is a rotation matrix?

A rotation matrix is a square matrix that represents a rotation in a multi-dimensional space. It is used to transform coordinates from one coordinate system to another, while preserving the distance between points and the angle between vectors.

How does a rotation matrix work?

A rotation matrix works by performing a rotation operation on a vector. The matrix is multiplied by the vector, resulting in a new vector with rotated coordinates. The amount of rotation is determined by the values in the matrix, which can be calculated using the desired angle of rotation.

What is the formula for a rotation matrix?

The formula for a 2D rotation matrix is:
[cosθ -sinθ]
[sinθ cosθ]
where θ is the angle of rotation. For higher dimensions, the formula becomes more complex, but the basic principle remains the same: the matrix is composed of trigonometric functions of the rotation angle.

What is the purpose of a rotation matrix?

The purpose of a rotation matrix is to simplify the process of rotating coordinates in a multi-dimensional space. It allows for easy transformation of vectors and points in a coordinate system, making it useful in fields such as computer graphics, robotics, and physics.

What are some applications of rotation matrices?

Rotation matrices have many applications, including 3D computer graphics, robotics, navigation systems, and physics simulations. They are also used in machine learning and data analysis to perform transformations on data points.

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