Linear Transformation Visualizer

2D matrix mappings

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About Linear Transformation Visualizer

A 2D linear transformation calculator. Apply a 2×2 matrix to input vectors and see the transformed result. Shows eigenvalues, determinant (area scaling), and transformation type. Select from preset transformations (rotation, shear, reflection, scaling). All calculations are client-side.

Linear Transformation Visualizer Features

  • Apply T(v)
  • Eigenvalues
  • det = area
  • Presets
  • Custom matrix
Linear transformation: T(v) = Av. A 2×2 matrix maps R² → R². det(A) = area scaling factor. Eigenvalues reveal stretching along invariant directions. Special cases: rotations (det=1, no real eigenvalues), reflections (det=−1), projections (det=0).

How to Use

Define the transformation:

  • Matrix: 2×2 entries or preset
  • Vector: Input v
  • Output: T(v) = Av

Types

  • Rotation: [[cos,-sin],[sin,cos]]
  • Shear: [[1,k],[0,1]]
  • Reflection: det = -1
  • Scaling: diagonal

Properties

T(αu+βv) = αT(u)+βT(v). Preserves origin, lines, parallelism. det(A) = area scaling. |det(A)| < 1 contracts, > 1 expands.

Step-by-Step Instructions

  1. 1Select a transformation.
  2. 2Enter input vector.
  3. 3View T(v) = Av.
  4. 4Check eigenvalues.
  5. 5Analyze determinant.

Linear Transformation Visualizer — Frequently Asked Questions

How do I identify the transformation type?+

det=1, orthogonal: rotation. det=-1, orthogonal: reflection. Diagonal: scaling. Upper/lower triangular with 1s: shear. det=0: projection (rank < 2). Symmetric: pure stretch along eigenvectors.

What do eigenvalues tell about a transformation?+

Eigenvalues = stretching factors along eigenvector directions. |λ|>1: stretches. |λ|<1: compresses. λ<0: reverses direction. Complex λ: rotation component. λ=1: invariant direction.

Why does determinant equal area scaling?+

The unit square maps to a parallelogram with area |det(A)|. Negative det means orientation reversal. det=0 means the image is a line or point (dimension collapse). This generalizes: det = volume scaling in nD.

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