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Algebra / English

✦ Diagonalization / Diagonalizing a Matrix, Part 1 ✦

Patrick J · YouTube · 7:25

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Explain diagonalization, simplify powers and state the independent-eigenvector criterion.

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Chapters

0:00A useful factorization3:00Cancel adjacent factors6:00Enough independent eigenvectors

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A diagonalization writes A=PDP−1A=PDP^{-1} with D diagonal and P invertible. Powers of D are calculated entry by entry.

In repeated products, P inverse followed by P is the identity. Therefore A4=PD4P−1A^4=PD^4P^{-1}, and the same reasoning works for any positive integer power.

Over the chosen field, an n-by-n matrix is diagonalizable exactly when it has n linearly independent eigenvectors. Put those vectors into P and their corresponding eigenvalues into D in matching order.

Knowledge cards

01

A useful factorization

A diagonalization writes A=PDP−1A=PDP^{-1} with D diagonal and P invertible. Powers of D are calculated entry by entry.

02

Cancel adjacent factors

In repeated products, P inverse followed by P is the identity. Therefore A4=PD4P−1A^4=PD^4P^{-1}, and the same reasoning works for any positive integer power.

03

Enough independent eigenvectors

Over the chosen field, an n-by-n matrix is diagonalizable exactly when it has n linearly independent eigenvectors. Put those vectors into P and their corresponding eigenvalues into D in matching order.

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  • Diagonalization ExplanationAt 0:00
    Why this connection?

    A diagonalization writes A=PDP−1A=PDP^{-1} with D diagonal and P invertible. Powers of D are calculated entry by entry. In repeated products, P inverse followed by P is the identity. Therefore A4=PD4P−1A^4=PD^4P^{-1}, and the same reasoning works for any positive integer power. Over the chosen field, an n-by-n matrix is diagonalizable exactly when it has n linearly independent eigenvectors. Put those vectors into P and their corresponding eigenvalues into D in matching order.