Linear Maps and Matrices

10.1 Basic Definitions and Properties of Linear Maps 143 K[t]_≤0andK are isomorphicK-vector spaces (of dimension 1). We already used this identification above. Similarly, we have identified the vector spaceVwithV¹and written justvinstead of(v)in Sect.9.3. Another common example in the literature is the notation Kⁿ that in our text denotes the set ofn-tuples with elements from K, but which is often used for the (matrix) sets of the “column vectors”K^n,1or the

“row vectors” K^1,n. The actual meaning then should be clear from the context. An attentive reader can significantly benefit from the simplifications due to such abuses of notation.

Thus, we can compute the coordinates of f(v)simply by multiplying the coordinates ofvwithA. This motivates the following definition.

Definition 10.14 The uniquely determined matrix in (10.1) is called thematrix rep- resentationof f ∈L(V,W)with respect to the basesB₁= {v₁, . . . , vm}ofVand

B₂= {w₁, . . . , wn}ofW. We denote this matrix by[f]B₁,B₂.

The construction of the matrix representation and Definition10.14can be consis- tently extended to the case that (at least) one of theK-vector spaces has dimension zero. If, for instance,m =dim(V)∈ NandW = {0}, then f(v_j)= 0 for every basis vectorv_j ofV. Thus, every vector f(v_j)is an empty linear combination of vector of the basis Ø ofW. The matrix representation of f then is an empty matrix of size 0×m. If alsoV= {0}, then the matrix representation of f is an empty matrix of size 0×0.

There are many different notations for the matrix representation of linear maps in the literature. The notation should reflect that the matrix depends on the linear map f and the given bases B₁ andB₂. Examples of alternative notations are[f]^B_B¹₂ and M(f)B1,B2(where “M” means “matrix”).

An important special case is obtained forV =W, hence in particularm=n, and f =IdV, the identity onV. We then obtain

(v1, . . . , vn)=(w1, . . . , wn)[IdV]B1,B2, (10.2) so that[IdV]_B1,B2is exactly the matrixPin (9.4), i.e., the coordinate transformation matrix in Theorem9.25. On the other hand,

(w₁, . . . , w_n)=(v₁, . . . , v_n)[IdV]_B2,B1,

and thus

[IdV]B1,B2

−1= [IdV]B2,B1.

Example 10.15

(1) Consider the vector spaceQ[t]_≤1 with the basesB₁ = {1,t}and B₂ = {t+ 1,t−1}. Then the linear map

f :Q[t]_≤1→Q[t]_≤1, α1t+α0 → 2α₁t+α0, has the matrix representations

[f]B1,B1 = 1 0

0 2

, [f]B1,B2 =

! ₁

2 1

−¹₂ 1

"

, [f]B2,B2 =

!₃

2 1 2 1 2

3 2

"

.

(2) For the vector spaceK[t]≤n with the basis B = {t⁰,t¹, . . . ,tⁿ}and the linear map

10.2 Linear Maps and Matrices 145

f : K[t]≤n →K[t]≤n,

αntⁿ+αn−1tⁿ⁻¹+. . .+α1t+α0 → α0tⁿ+α1tⁿ⁻¹+. . .+αn−1t+αn, we have f(t^j)=t^n−j for j =0,1, . . . ,n, so that

[f]B,B=

⎡

⎣ 1 . .. 1

⎤

⎦∈K^n+1,n+1.

Thus,[f]B,B is a permutation matrix.

Theorem 10.16 LetV and W be finite dimensional K -vector spaces with bases B₁= {v1, . . . , vm}and B₂= {w1, . . . , wn}, respectively. Then the map

L(V,W)→K^n,m, f → [f]_B1,B2,

is an isomorphism. HenceL(V,W)∼= K^n,manddim(L(V,W)) =dim(K^n,m)= n·m.

Proof In this proof we denote the map f → [f]B1,B2 by mat, i.e., mat(f) = [f]B1,B2. We first show that this map is linear. Let f,g∈L(V,W), mat(f)= [f_{i j}] and mat(g)= [gi j]. For j =1, . . . ,mwe have

(f +g)(vj)= f(vj)+g(vj)= n

i=1

f_{i j}wi + n

i=1

gi jwi = n

i=1

(f_{i j} +gi j)wi,

and thus mat(f +g)= [fi j +gi j] = [fi j] + [gi j] =mat(f)+mat(g). Forλ∈ K and j =1, . . . ,mwe have

(λf)(vj)=λf(vj)=λ n

i=1

fi jwi= n

i=1

(λfi j)wi,

and thus mat(λf)= [λfi j] =λ[fi j] =λmat(f).

It remains to show that mat is bijective. If f ∈ker(mat), i.e., mat(f)=0∈K^n,m, then f(vj)= 0 for j = 1, . . . ,m. Thus, f(v) =0 for allv ∈ V, so that f =0 (the zero map) and mat is injective (cp.(5)in Lemma10.7). If, on the other hand, A= [ai j] ∈ K^n,mis arbitrary, we define the linear map f :V →Wvia f(vj):=

n

i=1ai jwi, j =1, . . . ,m(cp. the proof of Theorem10.4). Then mat(f)= Aand hence mat is also surjective (cp.(4)in Lemma10.7).

Corollary10.12now shows that dim(L(V,W))=dim(K^n,m)=n·m(cp. also

Example9.20). ⊓⊔

Theorem10.16shows, in particular, that f,g ∈ L(V,W)satisfy f =g if and only if[f]B1,B2 = [g]B1,B2holds for given basesB₁ofVandB₂ofW. Thus, we can prove the equality of linear maps via the equality of their matrix representations.

We now consider the map from the elements of a finite dimensional vector space to their coordinates with respect to a given basis.

Lemma 10.17 If B= {v₁, . . . , vn}is a basis of a K -vector spaceV, then the map

B : V →K^n,1, v=λ1v1+. . .+λnvn→B(v):=

⎡

⎣ λ₁

... λn

⎤

⎦,

is an isomorphism, called thecoordinate mapofVwith respect to the basis B.

Proof The linearity ofB is clear. Moreover, we obviously haveB(V) = K^n,1, i.e.,B is surjective. Ifv∈ ker(B), i.e.,λ1 = · · · = λn =0, thenv =0, so that ker(B)= {0}andBis also injective (cp. (5) in Lemma10.7). ⊓⊔ Example 10.18 In the vector spaceK[t]≤nwith the basisB= {t⁰,t¹, . . . ,tⁿ}we have

B(αntⁿ+αn−1tⁿ⁻¹+. . .+α1t+α0)=

⎡

⎢⎢

⎣ α0

α1

... αn

⎤

⎥⎥

⎦∈Kⁿ⁺¹.

On the other hand, the basisB= {tⁿ,tⁿ⁻¹, . . . ,t⁰}yields

_B(αntⁿ+αn−1tⁿ⁻¹+. . .+α1t+α0)=

⎡

⎢⎢

⎣ αn

αn−1

... α0

⎤

⎥⎥

⎦∈ Kⁿ⁺¹.

IfB₁andB₂are bases of the finite dimensional vector spacesVandW, respec- tively, then we can illustrate the meaning and the construction of the matrix repre- sentation[f]B1,B2of f ∈L(V,W)in the followingcommutative diagram:

V

^{f //}

_B1

W

_B2

K

^m,1

[f]_B1,B2

//

K

^n,1

We see that different compositions of maps yield the same result. In particular, we have

f = ⁻¹_B

2 ◦ [f]B₁,B₂◦B₁, (10.3)

10.2 Linear Maps and Matrices 147 where the matrix[f]B1,B2 ∈ K^n,mis interpreted as a linear map fromK^m,1toK^n,1, and we use that the coordinate mapB2is bijective and hence invertible. In the same way we obtain

B2◦ f = [f]B1,B2◦B1, i.e.,

B2(f(v))= [f]_B1,B2B1(v) for allv∈V. (10.4) In words, the coordinates of f(v)with respect to the basisB₂ofWare given by the product of[f]_B1,B2and the coordinates ofvwith respect to the basisB₁ofV.

We next show that the consecutive application of linear maps corresponds to the multiplication of their matrix representations.

Theorem 10.19 LetV,Wand X be K -vector spaces. If f ∈ L(V,W)and g ∈ L(W,X), theng◦ f ∈L(V,X). Moreover, ifV,W andX are finite dimensional with respective bases B₁, B₂and B₃, then

[g◦ f]B₁,B₃ = [g]B₂,B₃[f]B₁,B₂.

Proof Leth :=g◦ f. We show first thath∈L(V,X). Foru, v∈Vandλ,µ∈ K we have

h(λu+µv)=g(f(λu+µv))=g(λf(u)+µf(v))

=λg(f(u))+µg(f(v))=λh(u)+µh(v).

Now let B₁ = {v₁, . . . , vm}, B₂ = {w₁, . . . , wn} and B₃ = {x₁, . . . ,xs}. If [f]B₁,B₂= [fi j]and[g]B₂,B₃ = [gi j], then for j =1, . . . ,mwe have

h(vj)=g(f(vj))=g _n

k=1

fk jwk

= n k=1

fk jg(wk)= n k=1

fk j

s i=1

gi kxi

= s

i=1

_n

k=1

fk jgi k

xi =

s i=1

_n

k=1

gi kfk j

% &' (

=:hi j

xi.

Thus,[h]B₁,B₃ = [hi j] = [gi j] [fi j] = [g]B₂,B₃[f]B₁,B₂. ⊓⊔ Using this theorem we can study how a change of the bases affects the matrix representation of a linear map.

Corollary 10.20 LetV and W be finite dimensional K -vector spaces with bases B₁,B₁ofVand B₂,B₂ofW. If f ∈L(V,W), then

[f]B1,B2 = [IdW]_B2,B2[f]_B1,B2[IdV]_B1,B1. (10.5) In particular, the matrices[f]B1,B2and[f]_B1,B2are equivalent.

Proof Applying Theorem10.19twice to the identity f =IdW◦ f ◦IdVyields [f]B1,B2= [(IdW◦ f)◦IdV]B1,B2

= [IdW◦ f]_B1,B2 [IdV]_B1,B1

= [IdW]_B2,B2[f]_B1,B2[IdV]_B1,B1.

The matrices[f]B1,B2and[f]_B1,B2are equivalent, since both[IdW]_B2,B2and[IdV]_B1,B1

are invertible. ⊓⊔

IfV =W,B₁=B₂, andB₁=B₂, then (10.5) becomes

[f]B1,B1= [IdV]_B1,B1[f]_B1,B1[IdV]_B1,B1 =([IdV]_B1,B1)⁻¹[f]_B1,B1[IdV]_B1,B1. Thus, the matrix representations[f]B1,B1 and[f]_B1,B1 of the endomorphism f ∈ L(V,V)aresimilar(cp. Definition8.11).

The following commutative diagram illustrates Corollary10.20:

K

^m,1

[f]B1,B2 //

[IdV]_B

1,B1

K

^n,1

V

^{f //}

_B

1

||yyyyyyyy

_B1

bbEE

EEEE EE

W

_B

2

""

EE EE EE EE

yy_By₂yyyyy^<<

K

^m,1

[f]_B

1,B2

//

K

^n,1

[IdW]_B

2,B2

OO (10.6)

Analogously to (10.3) we have f =⁻¹_B

2 ◦ [f]_B1,B2 ◦ _B1=⁻¹

B2 ◦ [f]_B1,B2 ◦ _B1. Example 10.21 For the following bases of the vector spaceQ^2,2,

B₁= 1 0

0 0

, 0 1

0 0

, 0 0

1 0

, 0 0

0 1

,

B₂= 1 0

0 1

, 1 0

0 0

, 1 1

0 0

, 0 0

1 0

,

10.2 Linear Maps and Matrices 149 we have the coordinate transformation matrices

[IdV]B1,B2=

⎡

⎢⎢

⎣

0 0 0 1

1−1 0−1

0 1 0 0

0 0 1 0

⎤

⎥⎥

⎦

and

[IdV]_B2,B1=([IdV]_B1,B2)⁻¹=

⎡

⎢⎢

⎣ 1 1 1 0 0 0 1 0 0 0 0 1 1 0 0 0

⎤

⎥⎥

⎦. The coordinate maps are

B₁

)a₁₁ a₁₂ a₂₁ a₂₂

*

=

⎡

⎢⎢

⎣ a₁₁ a₁₂ a₂₁ a₂₂

⎤

⎥⎥

⎦, B₂

)a₁₁a₁₂ a₂₁a₂₂

*

=

⎡

⎢⎢

⎣

a₂₂ a₁₁−a₁₂−a₂₂

a₁₂ a₂₁

⎤

⎥⎥

⎦,

and one can easily verify that

B2

)a₁₁ a₁₂ a₂₁ a₂₂

*

=([IdV]B1,B2◦B1)

)a₁₁ a₁₂ a₂₁ a₂₂

* .

Theorem 10.22 LetVandWbe K -vector spaces withdim(V)=m anddim(W)= n, respectively. Then there exist bases B₁ofVand B₂ofWsuch that

[f]B1,B2 = Ir 0

0 0

∈K^n,m,

where0≤r =dim(im(f))≤ min{n,m}. Furthermore, r =rank(F), where F is the matrix representation of f with respect to arbitrary bases ofVandW, and we definerank(f):=rank(F)=dim(im(f)).

Proof Let B₁ = {v₁, . . . ,vm}andB₂ = {w₁, . . . ,wn}be two arbitrary bases ofV andW, respectively. Let r := rank([f]_B1,B2). Then by Theorem5.11 there exist invertible matricesQ∈K^n,n andZ ∈K^m,mwith

Q[f]_B1,B2Z = Ir 0

0 0

, (10.7)

where r = rank([f]_B1,B2) ≤ min{n,m}. Let us introduce two new bases B₁ = {v₁, . . . , vm}andB₂= {w₁, . . . , wn}ofVandWvia

(v₁, . . . , vm):=(v₁, . . . ,vm)Z,

(w₁, . . . , wn):=(w₁, . . . ,wn)Q⁻¹, hence (w₁, . . . ,wn)=(w₁, . . . , wn)Q.

Then, by construction,

Z = [IdV]_B1,B1, Q= [IdW]_B2,B2. From (10.7) and Corollary10.20we obtain

Ir 0 0 0

= [IdW]_B2,B2[f]_B1,B2[IdV]_B1,B1 = [f]B₁,B₂.

We thus have found bases B₁ and B₂ that yield the desired matrix representation of f. Every other choice of bases leads, by Corollary10.20, to an equivalent matrix which therefore also has rankr. It remains to show thatr=dim(im(f)).

The structure of the matrix[f]B1,B2shows that

f(vj)= +

wj, 1≤ j≤r, 0, r+1≤ j ≤m.

Therefore,v_r+1, . . . , v_m∈ker(f), which implies that dim(ker(f))≥m−r. On the other hand,w₁, . . . , w_j∈im(f)and thus dim(im(f))≥r. Theorem10.9yields

dim(V)=m=dim(im(f)) +dim(ker(f)),

and hence dim(ker(f))=m−rand dim(im(f))=r. ⊓⊔ Example 10.23 ForA∈K^n,mand the corresponding mapA∈L(K^m,1,K^n,1)from (1) in Examples10.2and10.6, we have im(A)=span{a1, . . . ,a_m}. Thus, rank(A) is equal to the number of linearly independent columns of A. Since rank(A) = rank(A^T)(cp. (4) in Theorem5.11), this number is equal to the number of linearly independent rows ofA.

Theorem10.22is a first example of a general strategy that we will use several times in the following chapters:

By choosing appropriate bases, the matrix representation should reveal a desired information about a linear map in an efficient way.

In Theorem10.22this information is the rank of the linear map f, i.e., the dimen- sion of its image.

The dimension formula for linear maps can be generalized to the composition of maps as follows.

10.2 Linear Maps and Matrices 151 Theorem 10.24 If V, W and X are finite dimensional K -vector spaces,

f ∈L(V,W)andg∈L(W,X), then

dim(im(g◦ f))=dim(im(f))−dim(im(f)∩ker(g)).

Proof Letg:=g|im(f)be the restriction ofgto the image of f, i.e., the map g∈L(im(f),X), v→g(v).

Applying Theorem10.9togyields

dim(im(f))=dim(im(g))+dim(ker(g)).

Now

im(g)= {g(v)∈X|v∈im(f)} =im(g◦ f) and

ker(g)= {v∈im(f)|g(v)=0} =im(f)∩ker(g),

imply the assertion. ⊓⊔

Note that Theorem 10.22with V = W, f = IdV, and g ∈ L(V,X) gives dim(im(g))=dim(V)−dim(ker(g), which is equivalent to Theorem10.9.

If we interpret matrices A ∈ K^n,m and B ∈ K^s,n as linear maps, then Theo- rem10.24implies the equation

rank(B A)=rank(A)−dim(im(A)∩ker(B)).

For the special caseK =RandB= A^T we have the following result.

Corollary 10.25 If A∈R^n,m, thenrank(A^TA)=rank(A).

Proof Let w = [ω1, . . . ,ωn]^T ∈ im(A)∩ker(A^T). Thenw = Ay for a vector y∈R^m,1. Multiplying this equation from the left byA^T, and using thatw∈ker(A^T), we obtain 0= A^Tw=A^TAy, which implies

0=y^TA^TAy=w^Tw= n

j=1

ω²_j.

Since this holds only forw=0, we have im(A)∩ker(A^T)= {0}. ⊓⊔

Exercises

(In the following exercisesK is an arbitrary field.)

10.1 Consider the linear map onR^3,1 given by the matrixA =

⎡

⎣ 2 0 1 2 1 0 4 1 1

⎤

⎦∈ R^3,3. Determine ker(A), dim(ker(A))and dim(im(A)).

10.2 Construct a map f ∈ L(V,W)such that for linearly independent vectors v₁, . . . , vr ∈Vthe images f(v₁), . . . ,f(vr)∈Ware linearly dependent.

10.3 The map

f :R[t]_≤n→R[t]_≤n−1,

αntⁿ+α_n−1tⁿ⁻¹+. . .+α₁t+α₀ → nαntⁿ⁻¹+(n−1)αn−1tⁿ⁻²+. . .+2α2t+α₁, is called thederivative of the polynomial p ∈ R[t]≤n with respect to the variablet. Show that f is linear and determine ker(f)and im(f).

10.4 For the basesB₁ =

⎧⎨

⎩

⎡

⎣ 1 0 0

⎤

⎦,

⎡

⎣ 0 1 0

⎤

⎦,

⎡

⎣ 0 0 1

⎤

⎦

⎫⎬

⎭ofR^3,1andB₂= 1

0

, 0

1

of R^2,1, let f ∈ L(R^3,1,R^2,1) have the matrix representation [f]B₁,B₂ = 0 2 3

1−2 0

.

(a) Determine[f]_B1,B2 for the bases B₁ =

⎧⎨

⎩

⎡

⎣ 2 1

−1

⎤

⎦,

⎡

⎣ 1 0 3

⎤

⎦,

⎡

⎣

−1 2 1

⎤

⎦

⎫⎬

⎭of R^3,1andB2=

1 1

, 1

−1

ofR^2,1.

(b) Determine the coordinates of f([4,1,3]^T)with respect to the basisB₂. 10.5 Construct a map f ∈L(K[t],K[t])with the following properties:

(1) f(pq)=(f(p))q+p(f(q))for all p,q ∈K[t].

(2) f(t)=1.

Is this map uniquely determined by these properties or are there further maps with the same properties?

10.6 Letα∈ KandA∈K^n,n. Show that the maps

K[t] →K, p→ p(α), and K[t] →K^m,m, p→ p(A), are linear and justify the nameevaluation homomorphismfor this map.

10.7 LetS ∈G L_n(K). Show that the map f :K^n,n → K^n,n, A→S⁻¹ASis an isomorphism.

10.2 Linear Maps and Matrices 153

10.8 LetK be a field with 1+1=0 and letA∈K^n,n. Consider the map f :K^n,1→ K, x→x^TAx.

Is f a linear map? Show that f =0 if and only ifA+A^T =0.

10.9 LetV be a Q-vector space with the basis B1 = {v1, . . . , vn}and let f ∈ L(V,V)be defined by

f(v_j)=

+vj+v_j+1, j =1, . . . ,n−1, v1+vn, j =n.

(a) Determine[f]B1,B1.

(b) Let B₂ = {w₁, . . . , wn}withwj = jvn+1−j, j = 1, . . . ,n. Show that B₂ is a basis of V. Determine the coordinate transformation matrices [IdV]_B1,B2 and[IdV]_B2,B1, as well as the matrix representations[f]_B1,B2 and[f]_B2,B2.

10.10 Can you extend Theorem10.19consistently to the caseW = {0}? What are the properties of the matrices[g◦ f]B₁,B₃,[g]B₂,B₃and[f]B₁,B₂?

10.11 Consider the map f : R[t]≤n →R[t]_≤n+1,

αntⁿ+αn−1tⁿ⁻¹+. . .+α1t+α0 → 1

n+1αntⁿ⁺¹ + 1

n α_n−1tⁿ+. . .+1

2α₁t²+α₀t.

(a) Show that f is linear. Determine ker(f)and im(f).

(b) Choose bases B₁, B₂ in the two vector spaces and verify that for your choice rank([f]B₁,B₂)=dim(im(f))holds.

10.12 Letα1, . . . ,αn ∈R,n ≥2, be pairwise distinct numbers and letnpolynomials inR[t]be defined by

pj = ,n

k=1 k=j

) 1 αj−αk

(t−αk)

*

, j=1, . . . ,n.

(a) Show that the set B={p₁, . . . ,pn}is a basis ofR[t]_≤n−1. (This basis is called theLagrange basis²ofR[t]_≤n−1.)

(b) Show that the corresponding coordinate map is given by

2Joseph-Louis de Lagrange (1736–1813).

_B : R[t]_≤n−1→R^n,1, p→

⎡

⎣ p(α1)

... p(αn)

⎤

⎦.

(Hint:You can use Exercise7.8(b).)

10.13 Verify different paths in the commutative diagram (10.6) for the vector spaces and bases of Example10.21and linear map f :Q^2,2→Q^2,2,A→F Awith F=

1 1

−1 1

.

Chapter 11

Linear Forms and Bilinear Forms

In this chapter we study different classes of maps between one or twoK-vector spaces and the one dimensionalK-vector space defined by the field K itself. These maps play an important role in many areas of Mathematics, including Analysis, Functional Analysis and the solution of differential equations. They will also be essential for the further developments in this book: Using bilinear and sesquilinear forms, which are introduced in this chapter, we will define and study Euclidean and unitary vector spaces in Chap.12. Linear forms and dual spaces will be used in the existence proof of the Jordan canonical form in Chap.16.