Mathematics · Glossary

What is minimal polynomial?

Definition 3.11 University Mathematics — Year 2 · Chapter 3 — Reduction of Endomorphisms

For P=akXkK[X]P = \sum a_k X^k \in K[X], set P(u)=akukL(E)P(u) = \sum a_k u^k \in \mathcal{L}(E). The map PP(u)P \mapsto P(u) is a morphism of algebras K[X]L(E)K[X] \to \mathcal{L}(E) (Definition 1.33); its kernel {P:P(u)=0}\{P : P(u) = 0\} is an ideal of K[X]K[X], nonzero (the family (id,u,,un2)(\mathrm{id}, u, \dots, u^{n^2}) is linked in the n2n^2-dimensional L(E)\mathcal{L}(E)), hence generated by a unique monic polynomial μu\mu_u: the minimal polynomial (Theorem 1.26).

Examples

Example 3.13 (Minimal polynomials found by hand)

The minimal polynomial is computed by testing successive degrees. For the all-ones matrix JM3(R)J \in \mathcal{M}_3(\R): JλIJ \neq \lambda I (degree 11 is out), and J2=3JJ^2 = 3J, so

μJ=X23X=X(X3):\mu_J = X^2 - 3X = X(X - 3) :

degree 22, split, simple roots — JJ is diagonalizable with spectrum {0,3}\{0, 3\} (Corollary 3.17 below), confirming Example 2.19 without a single determinant. For the swap matrix AA of Example 3.15: A±IA \neq \pm I and A2=IA^2 = I give μA=X21\mu_A = X^2 - 1. In both cases the pattern is the same: guess a low-degree identity from the structure (rank one forces J2=(trJ)JJ^2 = (\operatorname{tr}J)\,J; an involution forces A2=IA^2 = I), then check that no proper divisor annihilates. Minimal polynomials are usually found, not computed from χ\chi.

Example 3.15 (The kernel lemma with explicit projectors)

Let A=(010100001)A = \left(\begin{smallmatrix}0 & 1 & 0\\ 1 & 0 & 0\\ 0 & 0 & 1\end{smallmatrix}\right) (swap the first two coordinates). Then A2=IA^2 = I: the polynomial X21=(X1)(X+1)X^2 - 1 = (X - 1)(X + 1) annihilates AA, its factors are coprime, and Bézout is explicit:

12(X+1)12(X1)=1.\frac{1}{2}(X + 1) - \frac12(X - 1) = 1 .

Following the proof of Theorem 3.14, the projections onto ker(AI)\ker(A - I) and ker(A+I)\ker(A + I) are the polynomials in AA

π+=A+I2=12(110110002),π=IA2=12(110110000).\pi_+ = \frac{A + I}{2} = \frac12\begin{pmatrix} 1 & 1 & 0\\ 1 & 1 & 0\\ 0 & 0 & 2\end{pmatrix}, \qquad \pi_- = \frac{I - A}{2} = \frac12\begin{pmatrix} 1 & -1 & 0\\ -1 & 1 & 0\\ 0 & 0 & 0\end{pmatrix}.

Check: π++π=I\pi_+ + \pi_- = I, π+π=0\pi_+\pi_- = 0, π±2=π±\pi_\pm^2 = \pi_\pm, and the images are the plane {x=y}\{x = y\} (symmetric vectors, eigenvalue 11) and the line R(1,1,0)\R(1, -1, 0) (antisymmetric, eigenvalue 1-1). The kernel lemma is not an existence statement: Bézout coefficients are the projector formulas.

Example 3.20 (Minimal versus characteristic)

For D=diag(2,2,3)D = \operatorname{diag}(2, 2, 3): χD=(X2)2(X3)\chi_D = (X - 2)^2(X - 3) but μD=(X2)(X3)\mu_D = (X - 2)(X - 3), since (D2I)(D3I)=0(D - 2I)(D - 3I) = 0 (check on the canonical basis) while neither factor alone kills DD. For the shift block N=(0100)(3)N = \left(\begin{smallmatrix}0 & 1\\ 0 & 0\end{smallmatrix}\right) \oplus (3), i.e. N=(010000003)N' = \left(\begin{smallmatrix}0 & 1 & 0\\ 0 & 0 & 0\\ 0 & 0 & 3\end{smallmatrix}\right): χN=X2(X3)\chi_{N'} = X^2(X - 3) and μN=X2(X3)\mu_{N'} = X^2(X - 3) — the double root is genuinely needed because NN' is not diagonalizable on ker\ker-side (Ne2=e10N'e_2 = e_1 \neq 0). Rule of thumb: μ\mu and χ\chi share their roots (Proposition 3.12); the multiplicity in μ\mu measures the size of the largest nilpotent block, the one in χ\chi the total dimension of the characteristic subspace.

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