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TheoremStatement: AI-adaptedProof: AI-generatedSession-authored (Fable 5 assisted)precheck passaudited 2026-08-11
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Similarity is an equivalence relation, and two matrices represent the same endomorphism in two bases exactly when they are similar

Statement

Similarity is an equivalence relation on Mn(F)M_n(F). Moreover, matrices A,BMn(F)A,B\in M_n(F) are similar if and only if there are an nn-dimensional FF-vector space VV, an endomorphism T:VVT:V\to V, and ordered bases B,C\mathcal B,\mathcal C such that A=[T]BBA=[T]_{\mathcal B}^{\mathcal B} and B=[T]CCB=[T]_{\mathcal C}^{\mathcal C}.

Facts & Assumptions

Given: A field FF, a natural nn, and matrices A,B,CMn(F)A,B,C\in M_n(F).

[L1]

AA and BB are similar when B=P1APB=P^{-1}AP for some invertible PP (Similar matrices: B=P1APB=P^{-1}AP for an invertible PP).

[L3]

The matrices Ei0E_{i0} are the standard coordinate columns in Mn×1(F)M_{n\times1}(F) (Matrix units EijE_{ij} and the Kronecker delta).

[L4]

Matrix multiplication is associative, unital, distributive, and compatible with scalar multiplication (Matrix multiplication is associative, unital, distributive, and compatible with scalar multiplication).

[L5]

A finite ordered list is an ordered basis exactly when it is linearly independent and spans the space (Basis of a vector space: a linearly independent spanning subset; and ordered basis: an injective finite list whose image is a basis).

Proof

technique · direct
1.1

Taking P=InP=I_n proves reflexivity. If B=P1APB=P^{-1}AP, then A=(P1)1BP1A=(P^{-1})^{-1}BP^{-1}, proving symmetry. If also C=Q1BQC=Q^{-1}BQ, then associativity in [L4] gives C=(PQ)1A(PQ)C=(PQ)^{-1}A(PQ), proving transitivity. Thus similarity is an equivalence relation.

givenL1L4
2.1

If AA and BB represent the same endomorphism in bases B\mathcal B and C\mathcal C, [L2] gives B=P1APB=P^{-1}AP with P=PBCP=P_{\mathcal B\leftarrow\mathcal C}, so they are similar.

step 1.1L1L2
3.1

Conversely, suppose B=P1APB=P^{-1}AP. On V=Mn×1(F)V=M_{n\times1}(F) let T=LAT=L_A, which is linear by [L4], and let E=(Ei0)i<n\mathcal E=(E_{i0})_{i<n}. By [L3], every column yy has the unique expansion y=i<nyiEi0y=\sum_{i<n}y_iE_{i0}, so [L5] makes E\mathcal E the standard ordered basis. The columns of invertible PP form an ordered basis C\mathcal C: independence follows by multiplying Px=0Px=0 by P1P^{-1}, and every column vector yy equals P(P1y)P(P^{-1}y), so [L5] applies. Moreover, [T]EE=A[T]_{\mathcal E}^{\mathcal E}=A because T(Ej0)T(E_{j0}) is the jj-th column of AA. The change matrix PECP_{\mathcal E\leftarrow\mathcal C} is PP, so [L2] gives [T]CC=P1AP=B[T]_{\mathcal C}^{\mathcal C}=P^{-1}AP=B.

step 2.1L1L2L3L4L5
4.1

Steps 2.1 and 3.1 prove both directions of the characterisation, including n=0n=0, where the unique empty matrix represents the unique endomorphism of the zero space.

step 2.1step 3.1L1L2L3L4L5

Depends on

Used by

Dependency tree · next 3 levels

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