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Exercise 133Linear algebra › Eigenvalues and diagonalisationL1Added 3 Nov 2026

Similar matrices have the same eigenvalues

#similarity#eigenvalues#characteristic polynomial

Statement

Let AA and B=P−1APB=P^{-1}AP be similar n×nn\times n matrices (PP invertible). Show that AA and BB have the same characteristic polynomial, hence the same eigenvalues with the same algebraic multiplicities, and that dim⁡Eλ(A)=dim⁡Eλ(B)\dim E_\lambda(A)=\dim E_\lambda(B) for every λ\lambda.

Hint

Compute det⁡(XI−B)\det(XI-B) by factoring P−1P^{-1} and PP. For the eigenspaces, see how P−1P^{-1} acts on them.

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Solution

Characteristic polynomial. Since P−1(XI)P=XIP^{-1}(XI)P=XI,
det⁡(XI−B)=det⁡(P−1(XI−A)P)=det⁡(P−1)det⁡(XI−A)det⁡(P)=det⁡(XI−A),\det(XI-B)=\det\big(P^{-1}(XI-A)P\big)=\det(P^{-1})\det(XI-A)\det(P)=\det(XI-A),
because det⁡(P−1)det⁡(P)=det⁡I=1\det(P^{-1})\det(P)=\det I=1.

Consequence. χA=χB\chi_A=\chi_B, so AA and BB have the same eigenvalues with the same multiplicities.

Eigenspaces. If Av=λvAv=\lambda v, then B(P−1v)=P−1APP−1v=P−1Av=λP−1vB(P^{-1}v)=P^{-1}APP^{-1}v=P^{-1}Av=\lambda P^{-1}v. So v↦P−1vv\mapsto P^{-1}v maps Eλ(A)E_\lambda(A) into Eλ(B)E_\lambda(B), and it is injective, so dim⁡Eλ(A)≤dim⁡Eλ(B)\dim E_\lambda(A)\le\dim E_\lambda(B). Since A=PBP−1A=PBP^{-1}, the same argument with PP gives the reverse inequality. Hence dim⁡Eλ(A)=dim⁡Eλ(B)\dim E_\lambda(A)=\dim E_\lambda(B).

Conclusion. Similar matrices have identical spectral data (eigenvalues, algebraic and geometric multiplicities).