Orthogonal Matrix Transformation
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Description
An orthogonal matrix Q satisfies Q^T Q = I, meaning its transpose equals its inverse. This special property makes orthogonal matrices preserve lengths and angles—they represent rotations and reflections that transform shapes without distortion.
Key Features:
- Real-Time Rotation: Drag the angle slider to see the unit square rotate smoothly
- Length Preservation: Toggle "Lengths" to verify that |Qv| = |v| for all vectors
- Angle Preservation: Toggle "Angles" to see that angles between vectors are maintained
- Reflection Toggle: Click "Reflect" to add a reflection (changes det(Q) from +1 to -1)
- Live Matrix Display: Watch the rotation matrix entries update as cos(θ) and sin(θ)
The Rotation Matrix
A 2D rotation by angle θ is represented by:
\[Q = \begin{bmatrix} \cos\theta & -\sin\theta \\ \sin\theta & \cos\theta \end{bmatrix}\]
Properties:
- Orthogonal: Q^T Q = Q Q^T = I
- Determinant: det(Q) = 1 (rotation) or -1 (reflection)
- Preserves Lengths: ||Qx|| = ||x|| for all vectors x
- Preserves Angles: The angle between Qx and Qy equals the angle between x and y
Lesson Plan
Learning Objectives
After using this MicroSim, students will be able to:
- Explain why orthogonal matrices preserve lengths and angles
- Write the 2D rotation matrix for any angle θ
- Distinguish between rotation (det = +1) and reflection (det = -1)
- Verify that Q^T Q = I for rotation matrices
Guided Exploration (5-7 minutes)
- Rotate the Square: Move the slider from 0° to 90° and observe the shape transformation
- Check Lengths: Enable "Lengths" and verify the sample vectors maintain their magnitude
- Check Angles: Enable "Angles" and verify the angle between vectors is preserved
- Add Reflection: Click "Reflect" and observe how the square flips
Key Discussion Points
- Why does the unit square maintain its shape during rotation?
- What happens to the determinant when reflection is added?
- How does this relate to preserving area?
Assessment Questions
- What is the rotation matrix for θ = 90°?
- If a rotation matrix has det = -1, is it a pure rotation?
- Why is Q^(-1) = Q^T computationally advantageous?
References
- Chapter 2: Matrices and Matrix Operations - Orthogonal matrices in context
- 3Blue1Brown: Linear Transformations - Visual understanding of transformations