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The Spectral Theorem L08-S00 The spectral theorem MATH 6610 Lecture 08 September 21, 2020 Trefethen & Bau: Lecture 24 MATH 6610-001 – U. Utah The spectral theorem When are matrices unitarily diagonalizable? Diagonalizability L08-S01 Recall: All non-defective square matrices are diagonalizable (eigenvalue decomposition) All square matrices are bidiagonalizable (Jordan normal form) All square matrices are unitarily triangularizable (Schur decomposition) MATH 6610-001 – U. Utah The spectral theorem Diagonalizability L08-S01 Recall: All non-defective square matrices are diagonalizable (eigenvalue decomposition) All square matrices are bidiagonalizable (Jordan normal form) All square matrices are unitarily triangularizable (Schur decomposition) When are matrices unitarily diagonalizable? MATH 6610-001 – U. Utah The spectral theorem Theorem (The spectral theorem) A square matrix A is unitarily diagonalizable if and only if it is normal. The spectral theorem L08-S02 Definition A matrix A P Cnˆn is a normal matrix if it commutes with its transpose. MATH 6610-001 – U. Utah The spectral theorem The spectral theorem L08-S02 Definition A matrix A P Cnˆn is a normal matrix if it commutes with its transpose. Theorem (The spectral theorem) A square matrix A is unitarily diagonalizable if and only if it is normal. MATH 6610-001 – U. Utah The spectral theorem If A is normal, ... then the 2-norm induced operator, spectral radius, and maximum numerical range coincide ˚ then }Ax}2 “}A x}2 for any vector x then A “ A˚U for some unitary U Actually, any of the above also implies that A is normal. Normal matrices L08-S03 Normal matrices are the class of matrices for which eigenvalues precisely characterize the action of a matrix. MATH 6610-001 – U. Utah The spectral theorem ˚ then }Ax}2 “}A x}2 for any vector x then A “ A˚U for some unitary U Actually, any of the above also implies that A is normal. Normal matrices L08-S03 Normal matrices are the class of matrices for which eigenvalues precisely characterize the action of a matrix. If A is normal, ... then the 2-norm induced operator, spectral radius, and maximum numerical range coincide MATH 6610-001 – U. Utah The spectral theorem then A “ A˚U for some unitary U Actually, any of the above also implies that A is normal. Normal matrices L08-S03 Normal matrices are the class of matrices for which eigenvalues precisely characterize the action of a matrix. If A is normal, ... then the 2-norm induced operator, spectral radius, and maximum numerical range coincide ˚ then }Ax}2 “}A x}2 for any vector x MATH 6610-001 – U. Utah The spectral theorem Actually, any of the above also implies that A is normal. Normal matrices L08-S03 Normal matrices are the class of matrices for which eigenvalues precisely characterize the action of a matrix. If A is normal, ... then the 2-norm induced operator, spectral radius, and maximum numerical range coincide ˚ then }Ax}2 “}A x}2 for any vector x then A “ A˚U for some unitary U MATH 6610-001 – U. Utah The spectral theorem Normal matrices L08-S03 Normal matrices are the class of matrices for which eigenvalues precisely characterize the action of a matrix. If A is normal, ... then the 2-norm induced operator, spectral radius, and maximum numerical range coincide ˚ then }Ax}2 “}A x}2 for any vector x then A “ A˚U for some unitary U Actually, any of the above also implies that A is normal. MATH 6610-001 – U. Utah The spectral theorem Theorem (Bauer-Fike) Assume A P Cnˆn is diagonalizable with eigenvector matrix V P Cnˆn. Then, using the 2-norm, the absolute condition number of computing eigenvalues is κλpAq “ κpV q. r Eigenvalue conditioning L08-S04 Consider A P Cnˆn, along with the map λ : Cnˆn Ñ C that computes an eigenvalue of A: Av “ λpAqv; for some v ‰ 0 What is the conditioning of this operation? MATH 6610-001 – U. Utah The spectral theorem Eigenvalue conditioning L08-S04 Consider A P Cnˆn, along with the map λ : Cnˆn Ñ C that computes an eigenvalue of A: Av “ λpAqv; for some v ‰ 0 What is the conditioning of this operation? Theorem (Bauer-Fike) Assume A P Cnˆn is diagonalizable with eigenvector matrix V P Cnˆn. Then, using the 2-norm, the absolute condition number of computing eigenvalues is κλpAq “ κpV q. r MATH 6610-001 – U. Utah The spectral theorem.
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