← Module 03 / Understanding matrices

Lesson 03.1 · §3.6

Which directions stay on the same line?

Identify eigenvectors and track eigenvalues under shifts, inverses, and powers.

In this lesson 3 ideas
  1. Direction can survive a transformation
  2. Proposition 3: shift, invert, repeat
  3. Transfer

Source scope: §3.6. Worked numerical examples, experiments, and practice are teaching supplements unless identified as source results.

Direction can survive a transformation

For A=diag⁡(2,−1)A=\operatorname{diag}(2,-1), the horizontal vector (1,0)(1,0) becomes (2,0)(2,0); the vertical vector (0,1)(0,1) becomes (0,−1)(0,-1). Both remain on their original lines. A negative scale reverses direction.

For a square real matrix AA, a nonzero vector xx is an eigenvector with eigenvalue λ\lambda if Ax=λxAx=\lambda x. Zero is excluded because A0=λ0A0=\lambda0 for every λ\lambda. A zero eigenvalue is allowed: a nonzero direction can collapse to zero.

Not every vector is an eigenvector. Here A(1,1)=(2,−1)A(1,1)=(2,-1) is not a scalar multiple of (1,1)(1,1). A real matrix need not have real eigenvectors: a quarter-turn rotation has no nonzero real vector on an unchanged line.

Proposition 3: shift, invert, repeat

Source statement and proof. Suppose Ax=λxAx=\lambda x with x≠0x\ne0.

  1. For any real γ\gamma, (A+γI)x=Ax+γx=(λ+γ)x(A+\gamma I)x=Ax+\gamma x=(\lambda+\gamma)x. The same eigenvector has shifted eigenvalue λ+γ\lambda+\gamma.
  2. If AA is invertible, x=A−1Ax=λA−1xx=A^{-1}Ax=\lambda A^{-1}x. Invertibility forces λ≠0\lambda\ne0, so division gives A−1x=λ−1xA^{-1}x=\lambda^{-1}x.
  3. For every nonnegative integer kk, Akx=λkxA^kx=\lambda^kx. The case k=0k=0 uses A0=IA^0=I; multiplying the kkth relation by AA proves the next. If AA is invertible, applying the same argument to A−1A^{-1} proves the relation for negative integers too.

Source clarification: Proposition 3(iii) prints k∈Zk\in\mathbb Z without repeating the invertibility condition. Negative powers require it. For k=0k=0, the scalar factor is one even when λ=0\lambda=0, by the identity-power convention.

Explore it yourself

Turn a unit vector

Predict the quadratic value before aligning x with an eigenvector.

Preparing interactive controls…

x₁x₂-4-4-3-3-2-2-1-1011223344q₁q₂xAx

A = [[2.500, 0.866], [0.866, 1.500]]. xᵀAx = R(x) = 3.000; bounds [1, 3]. trace = 4.000; determinant = 3.000. Positive definite.

Axes: first and second real coordinates, in unit steps. Endpoints and labels identify each vector.

Transfer

If Ax=−2xAx=-2x, what is (A+3I)x(A+3I)x? What is A2xA^2x?

Keep the same vector

They are xx and 4x4x. Squaring removes the reversal because two negative scalings produce a positive scaling. The inverse conclusion A−1x=−x/2A^{-1}x=-x/2 additionally requires the whole matrix to be invertible.