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A family of diagonalisable endomorphisms of a finite-dimensional space is simultaneously diagonalisable if and only if its members commute pairwise
Statement
Let be a family of diagonalisable endomorphisms of a finite-dimensional vector space. Then is simultaneously diagonalisable if and only if its members commute pairwise.
Facts & Assumptions
Given: A family of diagonalisable endomorphisms of a finite-dimensional space .
Commuting endomorphisms preserve each other's eigenspaces (Commuting endomorphisms preserve each other's eigenspaces).
The restriction of a diagonalisable endomorphism to an invariant subspace is diagonalisable (The restriction of a diagonalisable endomorphism to an invariant subspace is diagonalisable).
The space is finite-dimensional, with dimension ( and for finite-dimensional ).
is the intersection of all subspaces containing , hence the smallest such subspace (Linear combination of a finite list, and the span as the smallest linear subspace containing ); and , the set of finite linear combinations of elements of ( is exactly the set of linear combinations of finite lists of elements of , and ).
A diagonalisable endomorphism's distinct eigenspaces have direct sum equal to the whole space (An endomorphism is diagonalisable exactly when for some finite list of distinct scalars ).
Simultaneous diagonalisability means that one basis diagonalises every member of the family (Simultaneous diagonalisability: one basis that diagonalises every endomorphism in a family).
Proof
First suppose is finite and pairwise commuting. Induct on its size. For the empty family any basis works. For a nonempty family, choose one member . Its eigenspaces have direct sum by [L5]; by [L1] every remaining member preserves each eigenspace, and by [L2] every restriction is diagonalisable. Applying the induction hypothesis within each eigenspace and concatenating the resulting bases gives one common eigenbasis.
For an arbitrary pairwise commuting family, [L3] makes finite-dimensional. Choose a finite basis of ; by [L4], each basis vector is a finite linear combination of members of . The union of the finitely many supports is a finite subfamily spanning .
Conversely, if one basis diagonalises every member of as in [L6], then every pair is represented by diagonal matrices, which commute. The represented endomorphisms therefore commute.
Step 1.1 gives a common eigenbasis for . Every member of lies in its span, so it is represented by a linear combination of diagonal matrices in that basis and is diagonal too. Hence [L6] makes simultaneously diagonalisable.
Steps 2.1 and 1.3 prove both directions, including empty families and the zero space.
Depends on
- Simultaneous diagonalisability: one basis that diagonalises every endomorphism in a family
- Commuting endomorphisms preserve each other's eigenspaces
- The restriction of a diagonalisable endomorphism to an invariant subspace is diagonalisable
- An endomorphism is diagonalisable exactly when $V=\bigoplus_{i<r}E_{\lambda_i}(T)$ for some finite list of distinct scalars $\lambda_i$
- $\dim_F M_{m\times n}(F)=mn$ and $\dim_F\mathcal L(V,W)=(\dim_FV)(\dim_FW)$ for finite-dimensional $V,W$
- Linear combination of a finite list, and the span $\operatorname{span}(S)$ as the smallest linear subspace containing $S$
- $\operatorname{span}(S)$ is exactly the set of linear combinations of finite lists of elements of $S$, and $\operatorname{span}(\varnothing) = \{0_V\}$
Used by
- If two commuting endomorphisms are diagonalisable, then every finite linear combination of products of their powers is diagonalisable; in particular, their sum and product are diagonalisable Corollary
- Commuting alone does not imply simultaneous diagonalisability Counterexample
- Two commuting non-scalar matrices simultaneously diagonalised in one explicit basis Example
Dependency tree · next 3 levels
Direct dependencies and their dependencies through the next three levels: 94 results over 21 levels. An arrow runs from a result to what uses it, and this result sits at the bottom with a heavier outline. Click the chart to enlarge it.
Sources
- Keith Conrad, The Minimal Polynomial and Some Applications, Theorem 5.2 and Corollary 5.4 (standard reference, not scraped)