Eigenvalues, Eigenvectors, and Matrix
Decompositions
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A ⇒ q(λ) = |A − λI| is a polynomial of degree n.
n×n
For example,
a − λ
a12 11
A ⇒ |A − λI| = 2×2
a21
a22 − λ
= (a11 − λ)(a22 − λ) − a12 a21
= λ2 + (−a11 − a22 )λ + a11 a22 − a12 a21 .
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The definition of |A − λI| implies that the coefficient on λn is (−1)n .
a12
a13
a11 − λ
a
a22 − λ
a23
21
a31
a32
a33 − λ
..
.
..
..
.
.
an1
an2
an3
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···
···
···
..
.
···
a1n a2n a3n .. . ann − λ
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The n (not necessarily distinct) roots of q(λ) (i.e., solutions to q(λ) = 0)
are called the eigenvalues of A.
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The Fundamental Theorem of Algebra guarantees n roots, and the
Polynomial Factorization Theorem implies that
q(λ) = |A − λI| =
n
Y
(λi − λ)
i=1
for the eigenvalues λ1 , . . . , λn .
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Example:
"
Suppose A =
2 1
1 2
#
, then
2 − λ
1
|A − λI| = 1
2 − λ
= (2 − λ)(2 − λ) − 1
= λ2 − 4λ + 3
= (3 − λ)(1 − λ).
Eigenvalues are
λ1 = 3, λ2 = 1 ∵ (3 − λ)(1 − λ) = 0 ⇐⇒ λ = 3 or λ = 1.
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Example:
"
Suppose A =
3 0
0 3
#
, then
3 − λ
0 |A − λI| = 0
3 − λ
= (3 − λ)(3 − λ)
= λ2 − 6λ + 9.
Eigenvalues are λ1 = 3, λ2 = 3 ∵ (3 − λ)(3 − λ) = 0 ⇐⇒ λ = 3.
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In the previous example, the eigenvalue 3 is said to have
algebraic multiplicity 2.
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The algebraic multiplicity of eigenvalue λ∗ is
n
X
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11[λi = λ∗ ].
i=1
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Note that the roots of q(λ) are not necessarily real numbers.
For example,
"
A=
0 1
#
−1 0
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"
⇒ A − λI =
−λ
1
#
−1 −λ
⇒ |A − λI| = λ2 + 1
√
√
= ( −1 − λ)(− −1 − λ)
√
√
∴ λ1 = −1, λ2 = − −1.
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Ifn×n
A is symmetric, then all roots of q(λ) = |A − λI| are real numbers.
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λ is an eigenvalue of A
⇐⇒ |A − λI| = 0
⇐⇒ A − λI is singular
⇐⇒ rank(A − λI) < n
⇐⇒ ∃ x 6= 0 3 (A − λI)x = 0
⇐⇒ ∃ x 6= 0 3 Ax − λx = 0
⇐⇒ ∃ x 6= 0 3 Ax = λx.
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Such a vector x 6= 0 satisfying Ax = λx is called an eigenvector ofn×n
A
corresponding to eigenvalue λ.
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Note that if x is an eigenvector corresponding to eigenvalue λ, then cx
is also an eigenvector ∀ c ∈ R.
∵ Ax = λx ⇒ cAx = cλx ⇒ A(cx) = λ(cx).
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It is customary to take eigenvalues to be unit norm, i.e., we take
c=
1
kxk
so that the eigenvector cx satisfies
kcxk = 1.
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If λ is an eigenvalue ofn×n
A, the set of vectors satisfying Ax = λx is called
the eigenspace of the eigenvalue λ.
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Note that the eigenspace
{x : Ax = λx} = {x : (A − λI)x = 0} = N (A − λI).
The dimension of the eigenspace of λ is known as the
geometric multiplicity of λ.
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For symmetric matrices, it turns out that algebraic multiplicity of an
eigenvalue is the same as the geometric multiplicity.
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Show that eigenvectors corresponding to distinct eigenvalues of a
symmetric matrixn×n
A are orthogonal.
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Fact V7:
If S ⊆ Rn is a vector space of dimension k > 0, then there exist a set of
mutually orthonormal vectors that forms a basis for S.
Proof: Homework problem.
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It follows that for each distinct eigenvalue λ of a matrixn×n
A satisfying
A = A0 , there exists a set of orthonormal eigenvectors with
dim(N (A − λI)) elements.
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These orthonormal eigenvectors form a basis for the eigenspace of λ.
Furthermore, the collection of all such eigenvectors corresponding to
the eigenvalues of A is a mutually orthonormal set of n vectors.
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Let q1 , . . . , qn denote these n orthonormal eigenvectors corresponding
to eigenvalues λ1 , . . . , λn respectively.
Let Q = [q1 , . . . , qn ].
Let Λ = diag(λ1 , . . . , λn ).
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∵ q1 , . . . , qn are mutually orthonormal, Q0 Q = I.
Moreover, because Q is n × n, it follows that Q0 is Q−1 .
Thus,
QQ0 = Q0 Q = I.
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Note that
Aqi = λi qi ∀ i = 1, . . . , n
⇐⇒ AQ = QΛ
⇐⇒ AQQ0 = QΛQ0
0
⇐⇒ A = QΛQ =
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n
X
λi qi q0i .
i=1
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This result is known as the
Spectral Decomposition Theorem:
Ifn×n
A is a symmetric matrix, then A = QΛQ0 , where Q is an orthogonal
matrix and Λ is a diagonal matrix.
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Result A.19:
Letn×n
A be a symmetric matrix. Then rank(A) is equal to the number of
nonzero eigenvalues of A.
Proof: Homework problem.
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Supposen×n
A has eigenvalues λ1 , . . . , λn . Then
P
(a) trace(A) = ni=1 λi , and
(b) |A| =
Qn
i=1 λi .
Proof: Homework problem.
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Quadratic Forms
Supposen×n
A = [aij ] and x = [x1 , . . . , xn ]0 .
x0 Ax =
Pn
i=1
Pn
j=1 xi xj aij
is known as a quadratic form.
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Nonnegative Definite and Positive Definite Matrices
A symmetric matrixn×n
A is nonnegative definite (NND) if and only if
x0 Ax ≥ 0 ∀ x ∈ Rn .
A symmetric matrixn×n
A is positive definite (PD) if and only if
x0 Ax > 0 ∀ x ∈ Rn \ {0}.
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Supposen×n
A is a symmetric matrix.
Prove that A is NND if and only if all eigenvalues of A are nonnegative.
Prove that A is PD if and only if all eigenvalues of A are positive.
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Supposen×n
A is a symmetric NND matrix.
Show there exists a symmetric matrix B such that BB = A.
Such a matrix is called the symmetric square root of A and is denoted
by A1/2 .
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Result A.20 (Cholesky Decomposition):
Supposen×n
A is a symmetric matrix. n×n
A is PD iff there exists a nonsingular
lower triangular matrix L such that
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A = LL0 .
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(=⇒) If n = 1, A = [a]. Moreover,
1×1
A is PD ⇒ x0 ax > 0 ∀ x 6= 0
1×1
⇒ ax2 > 0 ∀ x 6= 0
⇒ a > 0.
√
If we take L = [ a], then LL0 = A, L is a nonsingular lower triangular
matrix, and the result holds.
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Now suppose the result holds for n = 1, . . . , k for some integer k ≥ 1.
Suppose A is any (k + 1) × (k + 1) matrix. Partition A as
A=
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"
#
Ak ak
a0k
a
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A is PD ⇒ Ak is PD ∵
x0 Ax > 0 ∀ x 6= 0
⇒ x0 Ax > 0 ∀ x 6= 0 with the last component 0
" #
y
0
> 0 ∀ y 6= 0
⇒ [y 0]A
0
⇒ y0 Ak y > 0 ∀ y 6= 0.
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∴ ∃ Lk nonsingular and lower triangular 3 Ak = Lk L0k .
Now let lk = L−1
k ak .
∵ A is PD,
h
l0k L−1
−1
k
#"
#
"
i A a
L0−1
k
k
k lk
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a0k
a
−1
>0⇒
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0 −1
⇒ l0k Lk−1 Ak L0−1
k lk − 2lk Lk ak + a > 0
0
⇒ l0k Lk−1 Lk L0k L0−1
k lk − 2lk lk + a > 0
⇒ −l0k lk + a > 0.
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Now let l =
p 0
−lk lk + a.
Then
L=
"
#
Lk 0
l0k
l
is lower triangular and nonsingular (∵ l > 0 ⇒ L is full rank) and
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0
LL =
=
#"
#
"
Lk 0 L0k lk
l0k
=
0
l
#
Lk l k
l0k L0k
l0k lk + l2
"
=
l
"
Lk L0k
Ak
Lk L−1
k ak
#
0
0
0
a0k L0−1
k Lk l k l k + a − l k l k
#
"
A k ak
a0k
a
= A.
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