Matrices and determinants

C
.

Matrix Algebra:
Determinants, Inverses,
Eigenvalues

C–1

Appendix C: MATRIX ALGEBRA: DETERMINANTS, INVERSES, EIGENVALUES C–2

This Chapter discusses more specialized properties of matrices, such as determinants, eigenvalues and
rank. These apply only to square matrices unless extension to rectangular matrices is explicitly stated.

§C.1 DETERMINANTS
The determinant of a square matrix A = [ai j ] is a number denoted by |A| or det(A), through which
important properties such as singularity can be briefly characterized. This number is defined as the
following function of the matrix elements:

|A| = ± a1 j1 a2 j2 . . . an jn , (C.1)

where the column indices j1 , j2 , . . . jn are taken from the set 1, 2, . . . , n with no repetitions allowed.
The plus (minus) sign is taken if the permutation ( j1 j2 . . . jn ) is even (odd).

EXAMPLE C.1
For a 2 × 2 matrix,
a11 a12
= a11 a22 − a12 a21 . (C.2)
a21 a22

EXAMPLE C.2
For a 3 × 3 matrix,

a11 a12 a13
a21 a22 a23 = a11 a22 a33 + a12 a23 a31 + a13 a21 a32 − a13 a22 a31 − a12 a21 a33 − a11 a23 a32 . (C.3)
a31 a32 a33

REMARK C.1
The concept of determinant is not applicable to rectangular matrices or to vectors. Thus the notation |x| for a
vector x can be reserved for its magnitude (as in Appendix A) without risk of confusion.

REMARK C.2
Inasmuch as the product (C.1) contains n! terms, the calculation of |A| from the definition is impractical for general
matrices whose order exceeds 3 or 4. For example, if n = 10, the product (C.1) contains 10! = 3, 628, 800 terms
each involving 9 multiplications, so over 30 million floating-point operations would be required to evaluate |A|
according to that definition. A more practical method based on matrix decomposition is described in Remark C.3.

§C.1.1 Some Properties of Determinants
Some useful rules associated with the calculus of determinants are listed next.
I. Rows and columns can be interchanged without affecting the value of a determinant. That is

|A| = |AT |. (C.4)

II. If two rows (or columns) are interchanged the sign of the determinant is changed. For example:

3 4 1 −2
=− . (C.5)
1 −2 3 4

C–2

C–3 §C.1 DETERMINANTS

III. If a row (or column) is changed by adding to or subtracting from its elements the corresponding
elements of any other row (or column) the determinant remains unaltered. For example:

3 4 3+1 4−2 4 2
= = = −10. (C.6)
1 −2 1 −2 1 −2

IV. If the elements in any row (or column) have a common factor α then the determinant equals
the determinant of the corresponding matrix in which α = 1, multiplied by α. For example:

6 8 3 4
=2 = 2 × (−10) = −20. (C.7)
1 −2 1 −2

V. When at least one row (or column) of a matrix is a linear combination of the other rows (or
columns) the determinant is zero. Conversely, if the determinant is zero, then at least one
row and one column are linearly dependent on the other rows and columns, respectively. For
example, consider
3 2 1
1 2 −1 . (C.8)
2 −1 3
This determinant is zero because the first column is a linear combination of the second and
third columns:
column 1 = column 2 + column 3 (C.9)
Similarly there is a linear dependence between the rows which is given by the relation

row 1 = 7
8
row 2 + 4
5
row 3 (C.10)

VI. The determinant of an upper triangular or lower triangular matrix is the product of the main
diagonal entries. For example,
3 2 1
0 2 −1 = 3 × 2 × 4 = 24. (C.11)
0 0 4
This rule is easily verified from the definition (C.1) because all terms vanish except j1 = 1,
j2 = 2, . . . jn = n, which is the product of the main diagonal entries. Diagonal matrices are a
particular case of this rule.
VII. The determinant of the product of two square matrices is the product of the individual determi-
nants:
|AB| = |A| |B|. (C.12)
This rule can be generalized to any number of factors. One immediate application is to matrix
powers: |A2 | = |A||A| = |A|2 , and more generally |An | = |A|n for integer n.
VIII. The determinant of the transpose of a matrix is the same as that of the original matrix:

|AT | = |A|. (C.13)

This rule can be directly verified from the definition of determinant.

C–3


REMARK C.3
Rules VI and VII are the key to the practical evaluation of determinants. Any square nonsingular matrix A (where
the qualifier “nonsingular” is explained in §C.3) can be decomposed as the product of two triangular factors

A = LU, (C.14)

where L is unit lower triangular and U is upper triangular. This is called a LU triangularization, LU factorization
or LU decomposition. It can be carried out in O(n 3 ) floating point operations. According to rule VII:

|A| = |L| |U|. (C.15)

But according to rule VI, |L| = 1 and |U| = u 11 u 22 . . . u nn . The last operation requires only O(n) operations.
Thus the evaluation of |A| is dominated by the effort involved in computing the factorization (C.14). For n = 10,
that effort is approximately 103 = 1000 floating-point operations, compared to approximately 3 × 107 from the
naive application of (C.1), as noted in Remark C.1. Thus the LU-based method is roughly 30, 000 times faster for
that modest matrix order, and the ratio increases exponentially for large n.

§C.1.2 Cramer’s Rule
Cramer’s rule provides a recipe for solving linear algebraic equations in terms of determinants. Let
the simultaneous equations be as usual denoted as

Ax = y, (C.16)

where A is a given n × n matrix, y is a given n × 1 vector, and x is the n × 1 vector of unknowns. The
explicit form of (C.16) is Equation (A.1) of Appendix A, with n = m.
The explicit solution for the components x1 , x2 . . ., xn of x in terms of determinants is

y1 a12 a13 ... a1n a11 y1 a13 ... a1n
y2 a22 a23 ... a2n a21 y2 a23 ... a2n
.
. .
. .. .
. .
. .
. .. .
.
. . . . . . . .
yn an2 an3 . . . ann an1 yn an3 . . . ann
x1 = , x2 = , ... (C.17)
|A| |A|

The rule can be remembered as follows: in the numerator of the quotient for x j , replace the j th column
of A by the right-hand side y.
This method of solving simultaneous equations is known as Cramer’s rule. Because the explicit
computation of determinants is impractical for n > 3 as explained in Remark C.3, this rule has
practical value only for n = 2 and n = 3 (it is marginal for n = 4). But such small-order systems
arise often in finite element calculations at the Gauss point level; consequently implementors should
be aware of this rule for such applications.

EXAMPLE C.3
Solve the 3 × 3 linear system
5 2 1 x1 8
3 2 0 x2 = 5 , (C.18)
1 0 2 x3 3

C–4

C–5 §C.2 SINGULAR MATRICES, RANK

by Cramer’s rule:
8 2 1 5 8 1 5 2 8
5 2 0 3 5 0 3 2 5
3 0 2 6 1 3 2 6 1 0 3 6
x1 = = = 1, x2 = = = 1, x3 = = = 1. (C.19)
5 2 1 6 5 2 1 6 5 2 1 6
3 2 0 3 2 0 3 2 0
1 0 2 1 0 2 1 0 2

EXAMPLE C.4
Solve the 2 × 2 linear algebraic system
2+β −β x1 5
= (C.20)
−β 1+β x2 0
by Cramer’s rule:
5 −β 2+β 5
0 1+β 5 + 5β −β 0 5β
x1 = = , x2 = = . (C.21)
2+β −β 2 + 3β 2+β −β 2 + 3β
−β 1+β −β 1+β

§C.1.3 Homogeneous Systems

One immediate consequence of Cramer’s rule is what happens if
y1 = y2 = . . . = yn = 0. (C.22)
The linear equation systems with a null right hand side
Ax = 0, (C.23)
is called a homogeneous system. From the rule (C.17) we see that if |A| is nonzero, all solution
components are zero, and consequently the only possible solution is the trivial one x = 0. The case in
which |A| vanishes is discussed in the next section.

§C.2 SINGULAR MATRICES, RANK
If the determinant |A| of a n × n square matrix A ≡ An is zero, then the matrix is said to be singular.
This means that at least one row and one column are linearly dependent on the others. If this row
and column are removed, we are left with another matrix, say An−1 , to which we can apply the same
criterion. If the determinant |An−1 | is zero, we can remove another row and column from it to get An−2 ,
and so on. Suppose that we eventually arrive at an r × r matrix Ar whose determinant is nonzero.
Then matrix A is said to have rank r , and we write rank(A) = r .
If the determinant of A is nonzero, then A is said to be nonsingular. The rank of a nonsingular n × n
matrix is equal to n.
Obviously the rank of AT is the same as that of A since it is only necessary to transpose “row” and
”column” in the deﬁnition.
The notion of rank can be extended to rectangular matrices as outlined in section §C.2.4 below. That
extension, however, is not important for the material covered here.

C–5


EXAMPLE C.5
The 3 × 3 matrix
3 2 2
A= 1 2 −1 , (C.24)
2 −1 3
has rank r = 3 because |A| = −3 = 0.

EXAMPLE C.6
The matrix
3 2 1
A= 1 2 −1 , (C.25)
2 −1 3
already used as an example in §C.1.1 is singular because its first row and column may be expressed as linear
combinations of the others through the relations (C.9) and (C.10). Removing the first row and column we are left
with a 2 × 2 matrix whose determinant is 2 × 3 − (−1) × (−1) = 5 = 0. Consequently (C.25) has rank r = 2.

§C.2.1 Rank Deficiency
If the square matrix A is supposed to be of rank r but in fact has a smaller rank r < r , the matrix is
¯
said to be rank deficient. The number r − r > 0 is called the rank deficiency.
¯

EXAMPLE C.7
Suppose that the unconstrained master stiffness matrix K of a finite element has order n, and that the element
possesses b independent rigid body modes. Then the expected rank of K is r = n − b. If the actual rank is less
than r , the finite element model is said to be rank-deficient. This is an undesirable property.

EXAMPLE C.8
An an illustration of the foregoing rule, consider the two-node, 4-DOF plane beam element stiffness derived in
Chapter 13:  
12 6L −12 6L
EI  4L 2 −6L 2L 2 
K= 3  (C.26)
L 12 −6L 
symm 4L 2
where E I and L are nonzero scalars. It can be verified that this 4 × 4 matrix has rank 2. The number of rigid
body modes is 2, and the expected rank is r = 4 − 2 = 2. Consequently this model is rank sufficient.

§C.2.2 Rank of Matrix Sums and Products
In finite element analysis matrices are often built through sum and product combinations of simpler
matrices. Two important rules apply to “rank propagation” through those combinations.
The rank of the product of two square matrices A and B cannot exceed the smallest rank of the
multiplicand matrices. That is, if the rank of A is ra and the rank of B is rb ,
Rank(AB) ≤ min(ra , rb ). (C.27)

Regarding sums: the rank of a matrix sum cannot exceed the sum of ranks of the summand matrices.
That is, if the rank of A is ra and the rank of B is rb ,
Rank(A + B) ≤ ra + rb . (C.28)

C–6

C–7 §C.3 MATRIX INVERSION

§C.2.3 Singular Systems: Particular and Homegeneous Solutions

Having introduced the notion of rank we can now discuss what happens to the linear system (C.16)
when the determinant of A vanishes, meaning that its rank is less than n. If so, the linear system (C.16)
has either no solution or an infinite number of solution. Cramer’s rule is of limited or no help in this
situation.
To discuss this case further we note that if |A| = 0 and the rank of A is r = n − d, where d ≥ 1 is the
rank deficiency, then there exist d nonzero independent vectors zi , i = 1, . . . d such that

Azi = 0. (C.29)

These d vectors, suitably orthonormalized, are called null eigenvectors of A, and form a basis for its
null space.
Let Z denote the n × d matrix obtained by collecting the zi as columns. If y in (C.13) is in the range
of A, that is, there exists an nonzero x p such that y = Ax p , its general solution is

x = x p + xh = x p + Zw, (C.30)

where w is an arbitrary d × 1 weighting vector. This statement can be easily verified by substituting
this solution into Ax = y and noting that AZ vanishes.
The components x p and xh are called the particular and homogeneous part, respectively, of the solution
x. If y = 0 only the homogeneous part remains.
If y is not in the range of A, system (C.13) does not generally have a solution in the conventional
sense, although least-square solutions can usually be constructed. The reader is referred to the many
textbooks in linear algebra for further details.
§C.2.4 Rank of Rectangular Matrices
The notion of rank can be extended to rectangular matrices, real or complex, as follows. Let A be m ×n. Its column
range space R(A) is the subspace spanned by Ax where x is the set of all complex n-vectors. Mathematically:
R(A) = {Ax : x ∈ C n }. The rank r of A is the dimension of R(A).
The null space N (A) of A is the set of n-vectors z such that Az = 0. The dimension of N (A) is n − r .
Using these definitions, the product and sum rules (C.27) and (C.28) generalize to the case of rectangular (but
conforming) A and B. So does the treatment of linear equation systems Ax = y in which A is rectangular; such
systems often arise in the fitting of observation and measurement data.
In finite element methods, rectangular matrices appear in change of basis through congruential transformations,
and in the treatment of multifreedom constraints.

§C.3 MATRIX INVERSION
The inverse of a square nonsingular matrix A is represented by the symbol A−1 and is defined by the
relation
AA−1 = I. (C.31)
The most important application of the concept of inverse is the solution of linear systems. Suppose
that, in the usual notation, we have
Ax = y (C.32)

C–7


Premultiplying both sides by A−1 we get the inverse relationship

x = A−1 y (C.33)

More generally, consider the matrix equation for multiple (m) right-hand sides:

A X = Y, (C.34)
n×n n×m n×m

which reduces to (C.32) for m = 1. The inverse relation that gives X as function of Y is

X = A−1 Y. (C.35)

In particular, the solution of
AX = I, (C.36)

is X = A−1 . Practical methods for computing inverses are based on directly solving this equation; see
Remark C.4.

§C.3.1 Explicit Computation of Inverses

The explicit calculation of matrix inverses is seldom needed in large matrix computations. But ocas-
sionally the need arises for the explicit inverse of small matrices that appear in element computations.
For example, the inversion of Jacobian matrices at Gauss points, or of constitutive matrices.
A general formula for elements of the inverse can be obtained by specializing Cramer’s rule to (C.36).
Let B = [bi j ] = A−1 . Then
A ji
bi j = , (C.37)
|A|

in which A ji denotes the so-called adjoint of entry ai j of A. The adjoint A ji is deﬁned as the determinant
of the submatrix of order (n − 1) × (n − 1) obtained by deleting the j th row and i th column of A,
multiplied by (−1)i+ j .
This direct inversion procedure is useful only for small matrix orders: 2 or 3. In the examples below
the inversion formulas for second and third order matrices are listed.

EXAMPLE C.9
For order n = 2:
a11 a12 1 a22 −a12
A= , A−1 = , (C.38)
a21 a22 |A| −a21 a22

in which |A| is given by (C.2).

C–8

C–9 §C.3 MATRIX INVERSION

EXAMPLE C.10
For order n = 3:
a11 a12 a13 1 b11 b12 b13
A= a21 a22 a23 , A−1 = b21 b22 b23 , (C.39)
a31 a32 a33 |A| b31 b32 b33
where
a22 a23 a12 a13 a12 a13
b11 = , b21 = − , b31 = ,
a32 a33 a32 a33 a22 a23
a a23 a11 a13 a a13
b12 = − 21 , b22 = , b32 = − 11 , (C.40)
a31 a33 a31 a33 a21 a23
a21 a22 a a12 a11 a12
b13 = , b23 = − 11 , b33 = ,
a31 a32 a31 a32 a21 a22
in which |A| is given by (C.3).

EXAMPLE C.11
2 4 2 1 1 −4 2
A= 3 1 1 , A−1 = − −2 0 4 . (C.41)
1 0 1 8 −1 4 −10

If the order exceeds 3, the general inversion formula based on Cramer’s rule becomes rapidly useless
as it displays combinatorial complexity. For numerical work it is preferable to solve the system (C.38)
after A is factored. Those techniques are described in detail in linear algebra books; see also Remark
C.4.

§C.3.2 Some Properties of the Inverse

I. The inverse of the transpose is equal to the transpose of the inverse:

(AT )−1 = (A−1 )T , (C.42)

because
(AA−1 ) = (AA−1 )T = (A−1 )T AT = I. (C.43)

II. The inverse of a symmetric matrix is also symmetric. Because of the previous rule, (AT )−1 =
A−1 = (A−1 )T , hence A−1 is also symmetric.
III. The inverse of a matrix product is the reverse product of the inverses of the factors:

(AB)−1 = B−1 A−1 . (C.44)

This is easily veriﬁed by substituting both sides of (C.39) into (C.31). This property generalizes
to an arbitrary number of factors.
IV. For a diagonal matrix D in which all diagonal entries are nonzero, D−1 is again a diagonal matrix
with entries 1/dii . The veriﬁcation is straightforward.

C–9


V. If S is a block diagonal matrix:
S 0 0 ... 0 
11
 0 S22 0 ... 0 
 
S= 0 0 S33 ... 0  = diag [ S ] , (C.45)
 . . . .. .  ii
 . . . . . 
. . . .
0 0 0 ... Snn

then the inverse matrix is also block diagonal and is given by
 S−1 0 0 ... 0 
11
 0 S−1 0 ... 0 
 22

S−1
= 0 0 S−1 ... 0  = diag [ S−1 ] . (C.46)
 . .
33
. .. .  ii
 . . . . . 
. . . .
0 0 0 ... S−1
nn

VI. The inverse of an upper triangular matrix is also an upper triangular matrix. The inverse of a
lower triangular matrix is also a lower triangular matrix. Both inverses can be computed in O(n 2 )
ﬂoating-point operations.

REMARK C.4
The practical numerical calculation of inverses is based on triangular factorization. Given a nonsingular n × n
matrix A, calculate its LU factorization A = LU, which can be obtained in O(n 3 ) operations. Then solve the
linear triangular systems:
UY = I, LX = Y, (C.47)

and the computed inverse A−1 appears in X. One can overwrite I with Y and Y with X. The whole process can be
completed in O(n 3 ) ﬂoating-point operations. For symmetric matrices the alternative decomposition A = LDLT ,
where L is unit lower triangular and D is diagonal, is generally preferred to save computing time and storage.

§C.4 EIGENVALUES AND EIGENVECTORS
Consider the special form of the linear system (C.13) in which the right-hand side vector y is a multiple
of the solution vector x:
Ax = λx, (C.48)

or, written in full,
a11 x1 + a12 x2 + · · · + a1n xn = λx1
a21 x2 + a22 x2 + · · · + a2n xn = λx2
(C.49)
··· ··· ··· ··· ···
an1 x1 + an2 x2 + · · · + ann xn = λxn

This is called the standard (or classical) algebraic eigenproblem. System (C.48) can be rearranged into
the homogeneous form
(A − λI) x = 0. (C.50)

C–10

C–11 §C.4 EIGENVALUES AND EIGENVECTORS

A nontrivial solution of this equation is possible if and only if the coefficient matrix A − λI is singular.
Such a condition can be expressed as the vanishing of the determinant
a11 − λ a12 ... a1n
a21 a22 − λ . . . a2n
|A − λI| = . . .. . = 0. (C.51)
.
. .
. . .
.
an1 an2 . . . ann − λ
When this determinant is expanded, we obtain an algebraic polynomial equation in λ of degree n:
P(λ) = λn + α1 λn−1 + · · · + αn = 0. (C.52)
This is known as the characteristic equation of the matrix A. The left-hand side is called the charac-
teristic polynomial. We known that a polynomial of degree n has n (generally complex) roots λ1 , λ2 ,
. . ., λn . These n numbers are called the eigenvalues, eigenroots or characteristic values of matrix A.
With each eigenvalue λi there is an associated vector xi that satisfies
Axi = λi xi . (C.53)
This xi is called an eigenvector or characteristic vector. An eigenvector is unique only up to a scale
factor since if xi is an eigenvector, so is βxi where β is an arbitrary nonzero number. Eigenvectors are
often normalized so that their Euclidean length is 1, or their largest component is unity.
§C.4.1 Real Symmetric Matrices
Real symmetric matrices are of special importance in the finite element method. In linear algebra
books dealing with the algebraic eigenproblem it is shown that:
(a) The n eigenvalues of a real symmetric matrix of order n are real.
(b) The eigenvectors corresponding to distinct eigenvalues are orthogonal. The eigenvectors corre-
sponding to multiple roots may be orthogonalized with respect to each other.
(c) The n eigenvectors form a complete orthonormal basis for the Euclidean space E n .
§C.4.2 Positivity
Let A be an n × n square symmetric matrix. A is said to be positive definite (p.d.) if
xT Ax > 0, x=0 (C.54)
A positive definite matrix has rank n. This property can be checked by computing the n eigenvalues
λi of Az = λz. If all λi > 0, A is p.d.
A is said to be nonnegative1 if zero equality is allowed in (C.54):
xT Ax ≤ 0, x=0 (C.55)
A p.d. matrix is also nonnegative but the converse is not necessarily true. This property can be checked
by computing the n eigenvalues λi of Az = λz. If r eigenvalues λi > 0 and n − r eigenvalues are zero,
A is nonnegative with rank r .
A symmetric square matrix A that has at least one negative eigenvalue is called indefinite.
1 A property called positive semi-definite by some authors.

C–11


§C.4.3 Normal and Orthogonal Matrices
Let A be an n × n real square matrix. This matrix is called normal if

AT A = AAT (C.56)

A normal matrix is called orthogonal if

AT A = AAT = I or AT = A−1 (C.57)

All eigenvalues of an orthogonal matrix have modulus one, and the matrix has rank n.
The generalization of the orthogonality property to complex matrices, for which transposition is re-
placed by conjugation, leads to unitary matrices. These are not required, however, for the material
covered in the text.

§C.5 THE SHERMAN-MORRISON AND RELATED FORMULAS
The Sherman-Morrison formula gives the inverse of a matrix modified by a rank-one matrix. The Woodbury
formula extends the Sherman-Morrison formula to a modification of arbitrary rank. In structural analysis these
formulas are of interest for problems of structural modifications, in which a finite-element (or, in general, a discrete
model) is changed by an amount expressable as a low-rank correction to the original model.

§C.5.1 The Sherman-Morrison Formula
Let A be a square n × n invertible matrix, whereas u and v are two n-vectors and β an arbitrary scalar. Assume
that σ = 1 + β vT A−1 u = 0. Then

−1 β −1 T −1
A + βuvT = A−1 − A uv A . (C.58)
σ

This is called the Sherman-Morrison formula2 when β = 1. Since any rank-one correction to A can be written as
βuvT , (C.58) gives the rank-one change to its inverse. The proof is by direct multiplication as in Exercise C.5.
For practical computation of the change one solves the linear systems Aa = u and Ab = v for a and b, using the
known A−1 . Compute σ = 1 + βvT a. If σ = 0, the change to A−1 is the dyadic −(β/σ )abT .

§C.5.2 The Woodbury Formula
Let again A be a square n × n invertible matrix, whereas U and V are two n × k matrices with k ≤ n and β an
arbitrary scalar. Assume that the k × k matrix Σ = Ik + βVT A−1 U, in which Ik denotes the k × k identity matrix,
is invertible. Then
−1
A + βUVT = A−1 − β A−1 UΣ−1 VT A−1 . (C.59)

This is called the Woodbury formula.3 It reduces to (C.58) if k = 1, in which case Σ ≡ σ is a scalar. The proof
is by direct multiplication.

2 J. Sherman and W. J. Morrison, Adjustment of an inverse matrix corresponding to changes in the elements of a given column
or a given row of the original matrix, Ann. Math. Statist., 20, (1949), 621. For a history of this remarkable formula and its
extensions, which are quite important in many applications such as statistics, see the paper by Henderson and Searle in SIAM
Review, 23, 53–60.
3 M.A.Woodbury, Inverting modified matrices, Statist. Res. Group, Mem. Rep. No. 42, Princeton Univ., Princeton, N.J.,
1950

C–12

C–13 §C.5 THE SHERMAN-MORRISON AND RELATED FORMULAS

§C.5.3 Formulas for Modiﬁed Determinants
Let A denote the adjoint of A. Taking the determinants from both sides of A + βuvT one obtains

|A + βuvT | = |A| + β vT A u. (C.60)

If A is invertible, replacing A = |A| A−1 this becomes

|A + βuvT | = |A| (1 + β vT A−1 u). (C.61)

Similarly, one can show that if A is invertible, and U and V are n × k matrices,

|A + βUVT | = |A| |Ik + β VT A−1 U|. (C.62)

C–13


Exercises for Appendix C: Determinants, Inverses, Eigenvalues

EXERCISE C.1
If A is a square matrix of order n and c a scalar, show that det(cA) = cn det A.

EXERCISE C.2
Let u and v denote real n-vectors normalized to unit length, so that uT = u = 1 and vT v = 1, and let I denote the
n × n identity matrix. Show that
det(I − uvT ) = 1 − vT u (EC.1)

EXERCISE C.3
Let u denote a real n-vector normalized to unit length, so that uT = u = 1 and I denote the n × n identity matrix.
Show that
H = I − 2uuT (EC.2)
is orthogonal: HT H = I, and idempotent: H2 = H. This matrix is called a elementary Hermitian, a Householder
matrix, or a reflector. It is a fundamental ingredient of many linear algebra algorithms; for example the QR
algorithm for finding eigenvalues.

EXERCISE C.4
n
The trace of a n × n square matrix A, denoted trace(A) is the sum i=1
aii of its diagonal coefficients. Show
that if the entries of A are real,
n n
trace(A A) =
T
ai2j (EC.3)
i=1 j=1

EXERCISE C.5
Prove the Sherman-Morrison formula (C.59) by direct matrix multiplication.

EXERCISE C.6
Prove the Sherman-Morrison formula (C.59) for β = 1 by considering the following block bordered system

A U B I
= n (EC.4)
VT Ik C 0

in which Ik and In denote the identy matrices of orders k and n, respectively. Solve (C.62) two ways: eliminating
first B and then C, and eliminating first C and then B. Equate the results for B.

EXERCISE C.7
Show that the eigenvalues of a real symmetric square matrix are real, and that the eigenvectors are real vectors.

EXERCISE C.8
Let the n real eigenvalues λi of a real n × n symmetric matrix A be classified into two subsets: r eigenvalues are
nonzero whereas n − r are zero. Show that A has rank r .

EXERCISE C.9
Show that if A is p.d., Ax = 0 implies that x = 0.

EXERCISE C.10
Show that for any real m × n matrix A, AT A exists and is nonnegative.

C–14

C–15 Exercises

EXERCISE C.11
Show that a triangular matrix is normal if and only if it is diagonal.

EXERCISE C.12
Let A be a real orthogonal matrix. Show that all of its eigenvalues λi , which are generally complex, have unit
modulus.

EXERCISE C.13
Let A and T be real n × n matrices, with T nonsingular. Show that T−1 AT and A have the same eigenvalues.
(This is called a similarity transformation in linear algebra).

EXERCISE C.14
(Tough) Let A be m × n and B be n × m. Show that the nonzero eigenvalues of AB are the same as those of BA
(Kahan).

EXERCISE C.15
Let A be real skew-symmetric, that is, A = −AT . Show that all eigenvalues of A are purely imaginary or zero.

EXERCISE C.16
Let A be real skew-symmetric, that is, A = −AT . Show that U = (I+A)−1 (I−A), called a Cayley transformation,
is orthogonal.

EXERCISE C.17
Let P be a real square matrix that satisfies
P2 = P. (EC.5)
Such matrices are called idempotent, and also orthogonal projectors. Show that all eigenvalues of P are either
zero or one.

EXERCISE C.18
The necessary and sufficient condition for two square matrices to commute is that they have the same eigenvectors.

EXERCISE C.19
A matrix whose elements are equal on any line parallel to the main diagonal is called a Toeplitz matrix. (They
arise in finite difference or finite element discretizations of regular one-dimensional grids.) Show that if T1 and
T2 are any two Toeplitz matrices, they commute: T1 T2 = T2 T1 . Hint: do a Fourier transform to show that the
eigenvectors of any Toeplitz matrix are of the form {eiωnh }; then apply the previous Exercise.

C–15

Matrices and determinants

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