λvv
Algebra · Concept hub

Eigenvalues and Eigenvectors

1822 CE19th-century France (Cauchy)

Through Eigenvalues and Eigenvectors: How can we recognize the same structure inside different problems?

Use rotations, permutations, matrices, and categories to read transformation rules and relationships rather than appearances.

This voyage is an editorial path for understanding, not a claim of direct historical influence or sole invention.

Understand it in one breath

A nonzero vector v that remains on the same line after multiplication by A, scaled by λ. A negative λ reverses orientation, and complex eigenvalues may be needed. PageRank models links as a stochastic transition matrix and approximates an eigenvector corresponding to its stationary distribution.

At a glance

Matrix A

Eigenvalue λ

Eigenvector v

Intuition

[[2,0],[0,3]]

2, 3

(1,0), (0,1)

Only the x- and y-axis directions stay fixed

[[3,1],[0,2]]

3, 2

(1,0), (1,−1)

Two tilted invariant axes

90° rotation [[0,-1],[1,0]]

Complex ±i

(1, ±i)

No real eigenvectors

PCA data covariance

Principal-component variance

Principal-axis direction

The data’s dominant directions

Google link matrix

1 (scalar)

PageRank vector

Stationary distribution of the web

Av = λv. Over the complex numbers, the characteristic polynomial of an n×n matrix has n roots counting multiplicity, but the matrix need not have n linearly independent eigenvectors.

Concept

Vectors a matrix only stretches without rotating. The "principal axes" of high-dimensional data — PageRank, PCA, quantum mechanics.

Key formula

Av=λvA\mathbf{v} = \lambda \mathbf{v}

Av = λv

Modern applications

PCA dimensionality reduction, PageRank, energy states in quantum mechanics, and vibration-mode analysis.

Beyond MathVoyage

Curated sources and problems. Bring one discovery back from OEIS, Project Euler, MathOverflow, or arXiv.

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Eigenvalues and Eigenvectors

Concepts opened from here

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