Apprendre Visualizing Linear Algebra Concepts

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Visualizing linear algebra concepts is a powerful way to build intuition and deepen your understanding of abstract mathematical ideas. When you see vectors as arrows on a plane, operations like addition and scalar multiplication become much more tangible. For instance, vector addition can be represented by placing vectors tip-to-tail, making it clear how their components combine. Similarly, matrix transformations can be visualized as movements, stretches, or rotations of vectors and shapes, allowing you to see exactly how a transformation alters space. These visual tools help bridge the gap between symbolic manipulation and geometric interpretation, making it easier to grasp concepts such as linear independence, span, and the effects of different types of matrices.


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import numpy as np
import matplotlib.pyplot as plt

# Define two vectors in 2D
v1 = np.array([2, 1])
v2 = np.array([1, 3])

# Plot the original vectors
plt.figure(figsize=(8, 8))
plt.quiver(0, 0, v1[0], v1[1], angles='xy', scale_units='xy', scale=1, color='r', label='v1')
plt.quiver(0, 0, v2[0], v2[1], angles='xy', scale_units='xy', scale=1, color='b', label='v2')

# Vector addition
v_sum = v1 + v2
plt.quiver(0, 0, v_sum[0], v_sum[1], angles='xy', scale_units='xy', scale=1, color='g', label='v1 + v2')

# Apply a matrix transformation (rotation by 45 degrees)
theta = np.radians(45)
rotation_matrix = np.array([[np.cos(theta), -np.sin(theta)],
                            [np.sin(theta),  np.cos(theta)]])
v1_rot = rotation_matrix @ v1
v2_rot = rotation_matrix @ v2

# Plot transformed vectors
plt.quiver(0, 0, v1_rot[0], v1_rot[1], angles='xy', scale_units='xy', scale=1, color='orange', label="Rotated v1", linestyle='dashed')
plt.quiver(0, 0, v2_rot[0], v2_rot[1], angles='xy', scale_units='xy', scale=1, color='purple', label="Rotated v2", linestyle='dashed')

plt.xlim(-1, 5)
plt.ylim(-1, 5)
plt.grid()
plt.legend()
plt.title("Visualizing Vectors and Matrix Transformations")
plt.xlabel("x")
plt.ylabel("y")
plt.show()

In the code above, you first create two vectors, v1 and v2, and plot them as arrows starting from the origin. The red and blue arrows represent the original vectors, showing their direction and length in the 2D plane. By calculating v1 + v2, you obtain a green arrow that visually demonstrates vector addition: its tip reaches the same point as if you placed the tail of v2 at the tip of v1. This makes the geometric meaning of vector addition clear.

Next, you define a rotation matrix that rotates vectors by 45 degrees. By multiplying this matrix with each vector, you get their rotated versions, drawn as orange and purple arrows. These show how a matrix transformation can change both the direction and magnitude of vectors. When you plot all these arrows together, you can directly compare the original and transformed vectors, making it easier to understand the effect of the transformation. The grid and axis labels help you interpret the coordinates and see exactly how the vectors move in space. This kind of visualization turns abstract algebraic operations into concrete geometric changes, making linear algebra much more accessible.

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Section 1. Chapitre 12

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Section 1. Chapitre 12