Chapter 5: Orthogonality Flashcards

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Theorem 5.3.1 Properties of inner products and norms in Rn. Let u, v, w ∈ Rn and c ∈ R. Then

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(a) u · v = v · u (commutativity of inner product)
(b) u · (v + w) = u · v + u · w (distributivity of inner product)
(c) (cu) · v = c(u · v)
(d) ||v||2 = v · v
(e) kvk ≥ 0 and kvk = 0 if and only if v = 0
(f) kcvk = |c|kvk

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Theorem 5.3.5 Let S = {v1, v2, . . . , vk} be an orthogonal set of non-zero vectors in Rn. Then

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S is a linearly independent set.

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Coefficients for orthogonal basis in
v = c1v1 + c2v2 + · · · + ckvk,

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ci =(v · vi)/(vi· vi)
, for i = 1, . . . , k

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Orthogonal matrix

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columns for an orthonormal set

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5
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Theorem 8.2.1 A square matrix Q is orthogonal if and only if

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Q^−1 = QT

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Theorem 10.4.3 Let Q be an n × n matrix. The following are equivalent,

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(a) Q is orthogonal
(b) ||Qx| = ||x|| for all x ∈ Rn
(c) Qx · Qy = x · y for all x, y ∈ Rn

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Theorem Ex 8.2.3 Let Q be an n × n orthogonal matrix. Then,

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(a) Q^−1 is orthogonal
(b) det Q = ±1
(c) If λ is an eigenvalue of Q, then |λ| = 1
(d) If Q1 and Q2 are orthogonal n × n matrices then so is Q1Q2

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8
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Definition Let A be an n × n matrix. A is orthogonally diagonalisable if

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there exists an orthogonal matrix Q and a diagonal
matrix D such that Q^TAQ = D.

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9
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Theorem 8.2.4 and 5.5.7 If A is a real symmetric matrix then

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a) The eigenvalues of A are all real.
b) Eigenvectors corresponding to distinct eigenvalues are orthogonal.

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10
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Theorem 8.2.2 Principal Axes Theorem If following conditions are equivalent for an n × n matrix A.

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a) A has an orthonormal set of n eigenvectors
b) A is orthogonally diagonalisable
c) A is symmetric

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11
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Definition A quadratic form in n variables is a function f : Rn →
R of the form

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f(x) = x
TAx = a11x^2_1 + a22x_2^2 + · · · +annx^2_n + ∑i<j 2aijxixj
where A is a symmetric n × n matrix and x∈ Rn. A is the matrix
associated with f .

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12
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x^TAx = 1 represents an ellipse if

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λ1 > 0 and λ2 > 0

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13
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x^TAx = 1 has no graph if

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λ1 < 0 and λ2 < 0

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14
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x^TAx = 1 represents an hyperbola if

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λ1 and λ2 have opposite signs.

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15
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Positive definite square matrix

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symmetric and all its eigenvalues λ are positive (negative), that is λ > 0

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16
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Negative definite square matrix

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Symmetric and all its eigenvalues λ are negative, that is λ < 0

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17
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Semidefinite

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If the eigenvalues λ ≥ 0 (λ ≤ 0) then the matrix positive (negative) semidefinite

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Indefinite

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eigenvalues take on both positive
and negative values

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Positive definite iff

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xTAx > 0 for every x =/= 0 in Rn
(signature is n)

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Positive semidefinite iff

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xTAx ≥ 0 for every x =/= 0 in Rn
(signature=rank)

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Negative definite iff

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x
TAx < 0 for every x 6= 0
in Rn
(signature is −n)

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Rank

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p + n where p is the number of positive eigenvalues and n is the number of negative eigenvalues

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Signature

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p-n where p is the number of positive eigenvalues and n is the number of negative eigenvalues

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Negative semidefinite iff

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xTAx ≤ 0 for every x =/= 0 in Rn
(signature= −rank)

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Indefinite iff
xTAx takes on both positive and negative values. (−rank
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Let u and v be vectors in C^n. Then u · v =
conjugate, transpose of u * v.
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Theorem 8.7.3 Let A, B denote complex n × n matrices and c ∈ C Then
a) (A^H)^H = A b) (A + B)^H = A^H + B^H c) (cA)^H = c conjugate A^H d) (AB)^H = B^H A^H
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Hermitian matrix
A square complex matrix A is called hermitian (selfadjoint) if A^H = A.
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Theorem 8.7.5 Let A be a complex n × n hermitian matrix.
a) Every eigenvalue of a hermitian matrix A is a real number. b) The eigenvectors corresponding to distinct eigenvalues of A are orthogonal.
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Unitary matrix
A square complex matrix U is called unitary if U^−1 = U^H.
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Theorem 8.7.6 Let U ∈ Mn×n(C) (the space of complex n × n matrices). The following are equivalent:
a) U is unitary matrix b) The columns of U form an orthonormal basis for Cn with respect to the complex dot product c) ||Ux|| = ||x|| for every x ∈ Cn d) Ux · Uy = x · y for every x, y ∈ Cn
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Unitary diagonalisable
A square complex matrix A is called unitary diagonalisable if there exists a unitary matrix U and diagonal matrix D such that UH AU = D.
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Spectral Theorem
Every hermitian matrix A is unitarily diagonalisable.
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Are all unitarily diagonalisable matrices Hermitian?
No
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Are all hermitian matrices unitarily diagonalisable?
Yes
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Principal axis theorem asserts that
a real matrix is symmetric if and only if it is orthogonally diagonalisable
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Are all orthogonally diagonalisable matrices symmetric?
Yes
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Are all symmetric matrices orthogonally diagonalisable?
Yes
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Normal matrix
A square complex matrix A is called normal if A^H A = A A^H
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Theorem 8.7.9 A square complex matrix A is unitarily diagonalisable if and only if
A is normal, that is A^H A = A A^H
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Matrices that are normal
Every hermitian matrix, every unitary matrix, and every skew hermitian matrix (A^H = −A) is normal.