Diagonalization and eigenspace multiplicities
A real n×n matrix A is diagonalizable over ℝ exactly when it has n linearly independent real eigenvectors.
Key knowledge
A real n×n matrix A is diagonalizable over ℝ exactly when it has n linearly independent real eigenvectors. With eigenvectors as columns of P, P⁻¹AP is diagonal with their corresponding eigenvalues. Distinct n real eigenvalues suffice; more generally the characteristic polynomial must split over ℝ and each eigenspace dimension equal its eigenvalue's algebraic multiplicity.
Conditions and scope
- n≥1; P must be invertible, with eigenvalues listed in the same order as its eigenvector columns.
- Repeated eigenvalues do not automatically prevent diagonalization. A real matrix may lack a real eigenbasis; this statement does not confuse diagonalizability with invertibility or claim numerical stability.
Sources · 1
References
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Evidence and review
Section 5.4.1 diagonalization theorem and two-way basis/column proof; distinct-eigenvalue consequence; section 5.4.3 multiplicity variant and proof. The source's real-field splitting requirement is retained.
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