Linear Mappings

Linear Algebra

2.7 Linear Mappings

A=





1 2 1

−2 −3 1

3 5 0



.

We use Gaussian elimination to determine the rank:





1 2 1

−2 −3 1

3 5 0



 ⇝· · ·⇝





1 2 1 0 1 3 0 0 0



 . (2.84)

Here, we see that the number of linearly independent rows and columns is 2, such thatrk(A) = 2.

2.7 Linear Mappings 49 If Φ is surjective, then every element inW can be “reached” from V usingΦ. A bijective Φcan be “undone”, i.e., there exists a mappingΨ : W → V^{so that}Ψ◦Φ(x) =x. This mappingΨis then called the inverse ofΦand normally denoted byΦ⁻¹.

With these definitions, we introduce the following special cases of linear mappings between vector spacesV andW:

isomorphism

Isomorphism:Φ :V →W linear and bijective endomorphism

Endomorphism:Φ :V →V linear automorphism

Automorphism:Φ :V →V linear and bijective

We define idV : V → V, x 7→ x as the identity mapping or identity identity mapping identity automorphism

automorphisminV.

Example 2.19 (Homomorphism)

The mappingΦ :R²→C, Φ(x) =x1+ix2, is a homomorphism:

Φ x1

x2

+

y1

y2

= (x1+y1) +i(x2+y2) =x1+ix2+y1+iy2

= Φ x1

x2

+ Φ

y1

y2

Φ

λ x1

x2

=λx1+λix2=λ(x1+ix2) =λΦ x1

x2

.

(2.88) This also justifies why complex numbers can be represented as tuples in R²: There is a bijective linear mapping that converts the elementwise addi- tion of tuples inR²into the set of complex numbers with the correspond- ing addition. Note that we only showed linearity, but not the bijection.

Theorem 2.17(Theorem 3.59 in Axler (2015)). Finite-dimensional vector spacesV andW are isomorphic if and only ifdim(V) = dim(W).

Theorem 2.17 states that there exists a linear, bijective mapping be- tween two vector spaces of the same dimension. Intuitively, this means that vector spaces of the same dimension are kind of the same thing, as they can be transformed into each other without incurring any loss.

Theorem 2.17 also gives us the justification to treatR^m×n (the vector space ofm×n-matrices) andR^mn(the vector space of vectors of length mn) the same, as their dimensions are mn, and there exists a linear, bi- jective mapping that transforms one into the other.

Remark. Consider vector spacesV, W, X. Then:

For linear mappings Φ : V → W and Ψ : W → X, the mapping Ψ◦Φ :V →Xis also linear.

If Φ : V → W is an isomorphism, then Φ⁻¹ : W → V is an isomor- phism, too.

Figure 2.8 Two different coordinate systems defined by two sets of basis vectors. A vectorx has different coordinate representations depending on which coordinate system is chosen.

x x

e1

e2

b1

b2

IfΦ :V →W, Ψ :V →W are linear, thenΦ + ΨandλΦ, λ∈R, are linear, too.

♢

2.7.1 Matrix Representation of Linear Mappings

Anyn-dimensional vector space is isomorphic toRⁿ (Theorem 2.17). We consider a basis{b1, . . . ,bn} ^{of an}n-dimensional vector spaceV. In the following, the order of the basis vectors will be important. Therefore, we write

B= (b1, . . . ,bn) (2.89) and call thisn-tuple anordered basisofV.

ordered basis

Remark(Notation). We are at the point where notation gets a bit tricky.

Therefore, we summarize some parts here.B = (b1, . . . ,bn)is an ordered basis,B={b1, . . . ,bn}is an (unordered) basis, andB = [b1, . . . ,bn]is a matrix whose columns are the vectorsb1, . . . ,bn. ♢ Definition 2.18(Coordinates). Consider a vector spaceV and an ordered basisB = (b1, . . . ,bn)ofV. For anyx∈V we obtain a unique represen- tation (linear combination)

x=α1b1+. . .+αnbn (2.90) of x with respect to B. Then α1, . . . , αn are the coordinates of x with

coordinate

respect toB, and the vector

α=





 α1

... αn





∈Rⁿ (2.91)

is thecoordinate vector/coordinate representationofxwith respect to the

coordinate vector coordinate

representation ordered basisB.

2.7 Linear Mappings 51 A basis effectively defines a coordinate system. We are familiar with the Cartesian coordinate system in two dimensions, which is spanned by the canonical basis vectorse1,e2. In this coordinate system, a vectorx∈R² has a representation that tells us how to linearly combine e1 and e2 to obtain x. However, any basis of R² defines a valid coordinate system, and the same vector xfrom before may have a different coordinate rep- resentation in the(b1,b2)basis. In Figure 2.8, the coordinates of xwith respect to the standard basis(e1,e2) is[2,2]^⊤. However, with respect to the basis (b1,b2) the same vector x is represented as [1.09,0.72]^⊤, i.e., x = 1.09b1+ 0.72b2. In the following sections, we will discover how to obtain this representation.

Example 2.20

Let us have a look at a geometric vectorx ∈R²with coordinates[2,3]^⊤ Figure 2.9 Different coordinate representations of a vectorx, depending on the choice of basis.

e1

e2

b2

b1

x=−¹₂b1+⁵₂b2

x= 2e1+ 3e2

with respect to the standard basis(e1,e2)ofR². This means, we can write x= 2e1+ 3e2. However, we do not have to choose the standard basis to represent this vector. If we use the basis vectorsb1= [1,−1]^⊤,b2= [1,1]^⊤ we will obtain the coordinates ¹₂[−1,5]^⊤to represent the same vector with respect to(b1,b2)(see Figure 2.9).

Remark. For an n-dimensional vector space V and an ordered basis B of V, the mapping Φ : Rⁿ → V, Φ(ei) = bi, i = 1, . . . , n, is linear (and because of Theorem 2.17 an isomorphism), where (e1, . . . ,en) is the standard basis ofRⁿ.

♢ Now we are ready to make an explicit connection between matrices and linear mappings between finite-dimensional vector spaces.

Definition 2.19 (Transformation Matrix). Consider vector spaces V, W with corresponding (ordered) basesB = (b1, . . . ,bn)andC = (c1, . . . ,cm). Moreover, we consider a linear mappingΦ :V →W. Forj∈ {1, . . . , n}^,

Φ(bj) =α1jc1+· · ·+αmjcm=

m

X

i=1

αijci (2.92) is the unique representation ofΦ(bj)with respect toC. Then, we call the m×n-matrixAΦ, whose elements are given by

AΦ(i, j) =αij, (2.93)

thetransformation matrixofΦ(with respect to the ordered basesB ofV transformation matrix

andCofW).

The coordinates of Φ(bj) with respect to the ordered basis C of W are the j-th column of AΦ. Consider (finite-dimensional) vector spaces V, W with ordered bases B, C and a linear mapping Φ :V →W with

transformation matrixAΦ. If xˆ is the coordinate vector of x ∈ V with respect toB andyˆ the coordinate vector ofy= Φ(x) ∈W with respect toC, then

ˆ

y=AΦxˆ. (2.94)

This means that the transformation matrix can be used to map coordinates with respect to an ordered basis in V to coordinates with respect to an ordered basis inW.

Example 2.21 (Transformation Matrix)

Consider a homomorphism Φ : V → W and ordered bases B = (b1, . . . ,b3)ofV andC= (c1, . . . ,c4)ofW. With

Φ(b1) =c1−c2+ 3c3−c4

Φ(b2) = 2c1+c2+ 7c3+ 2c4

Φ(b3) = 3c2+c3+ 4c4

(2.95)

the transformation matrixAΦwith respect toB andC satisfiesΦ(bk) = P4

i=1αikci fork= 1, . . . ,3and is given as AΦ= [α1,α2,α3] =









1 2 0

−1 1 3 3 7 1

−1 2 4









, (2.96)

where theαj, j= 1,2,3,are the coordinate vectors ofΦ(bj)with respect toC.

Example 2.22 (Linear Transformations of Vectors)

Figure 2.10 Three examples of linear transformations of the vectors shown as dots in (a);

(b) Rotation by45^◦; (c) Stretching of the horizontal

coordinates by2;

(d) Combination of reflection, rotation and stretching.

(a) Original data. (b) Rotation by45^◦. (c) Stretch along the horizontal axis.

(d) General linear mapping.

We consider three linear transformations of a set of vectors inR²with the transformation matrices

A1=

cos(^π₄) −sin(^π₄) sin(^π₄) cos(^π₄)

, A2 = 2 0

0 1

, A3= 1 2

3 −1 1 −1

. (2.97)

2.7 Linear Mappings 53 Figure 2.10 gives three examples of linear transformations of a set of vec- tors. Figure 2.10(a) shows400vectors inR², each of which is represented by a dot at the corresponding (x1, x2)-coordinates. The vectors are ar- ranged in a square. When we use matrixA1in (2.97) to linearly transform each of these vectors, we obtain the rotated square in Figure 2.10(b). If we apply the linear mapping represented byA2, we obtain the rectangle in Figure 2.10(c) where eachx1-coordinate is stretched by2. Figure 2.10(d) shows the original square from Figure 2.10(a) when linearly transformed usingA3, which is a combination of a reflection, a rotation, and a stretch.

2.7.2 Basis Change

In the following, we will have a closer look at how transformation matrices of a linear mappingΦ : V →W change if we change the bases inV and W. Consider two ordered bases

B= (b1, . . . ,bn), B˜ = (˜b1, . . . ,˜bn) (2.98) ofV and two ordered bases

C= (c1, . . . ,cm), C˜ = (˜c1, . . . ,˜cm) (2.99) of W. Moreover, AΦ ∈ R^m×n is the transformation matrix of the linear mappingΦ :V →W with respect to the basesBandC, andA˜Φ∈^Rm×n is the corresponding transformation mapping with respect to B˜ and C˜. In the following, we will investigate howAandA˜ are related, i.e., how/

whether we can transform AΦ into A˜Φ if we choose to perform a basis change fromB, C toB,˜ C˜.

Remark. We effectively get different coordinate representations of the identity mapping idV. In the context of Figure 2.9, this would mean to map coordinates with respect to(e1,e2)onto coordinates with respect to (b1,b2) without changing the vectorx. By changing the basis and corre- spondingly the representation of vectors, the transformation matrix with respect to this new basis can have a particularly simple form that allows

for straightforward computation. ♢

Example 2.23 (Basis Change) Consider a transformation matrix

A= 2 1

1 2

(2.100) with respect to the canonical basis inR². If we define a new basis

B = ( 1

1

, 1

−1

) (2.101)

we obtain a diagonal transformation matrix A˜ =

3 0 0 1

(2.102) with respect toB, which is easier to work with thanA.

In the following, we will look at mappings that transform coordinate vectors with respect to one basis into coordinate vectors with respect to a different basis. We will state our main result first and then provide an explanation.

Theorem 2.20(Basis Change). For a linear mappingΦ :V →W, ordered bases

B = (b1, . . . ,bn), B˜ = (˜b1, . . . ,˜bn) (2.103) ofV and

C= (c1, . . . ,cm), C˜ = (˜c1, . . . ,˜cm) (2.104) ofW, and a transformation matrix AΦof Φwith respect toB and C, the corresponding transformation matrixA˜Φwith respect to the basesB˜andC˜ is given as

A˜Φ=T⁻¹AΦS. (2.105)

Here,S ∈R^n×n is the transformation matrix ofidV that maps coordinates with respect toB˜ onto coordinates with respect toB, andT ∈^{Rm×mis the} transformation matrix ofidW that maps coordinates with respect toC˜ onto coordinates with respect toC.

Proof Following Drumm and Weil (2001), we can write the vectors of the new basisB˜ of V as a linear combination of the basis vectors ofB, such that

˜bj =s1jb1+· · ·+snjbn =

n

X

i=1

sijbi, j= 1, . . . , n . (2.106) Similarly, we write the new basis vectorsC˜of W as a linear combination of the basis vectors ofC, which yields

˜

ck=t1kc1+· · ·+tmkcm=

m

X

l=1

tlkcl, k= 1, . . . , m . (2.107) We define S = ((sij)) ∈ R^n×n as the transformation matrix that maps coordinates with respect to B˜ onto coordinates with respect to B and T = ((tlk))∈ R^m×mas the transformation matrix that maps coordinates with respect toC˜onto coordinates with respect toC. In particular, thejth column ofS is the coordinate representation of˜bj with respect toB and

2.7 Linear Mappings 55 thekth column ofT is the coordinate representation of˜ckwith respect to C. Note that bothSandT are regular.

We are going to look atΦ(˜bj)from two perspectives. First, applying the mappingΦ, we get that for allj= 1, . . . , n

Φ(˜bj) =

m

X

k=1

˜ akj˜ck

| {z }

∈W (2.107)

=

m

X

k=1

˜ akj

m

X

l=1

tlkcl=

m

X

l=1 m

X

k=1

tlk˜akj

!

cl, (2.108)

where we first expressed the new basis vectors ˜ck ∈ W as linear com- binations of the basis vectors cl ∈ W and then swapped the order of summation.

Alternatively, when we express the ˜bj ∈ V as linear combinations of bj∈V, we arrive at

Φ(˜bj)^(2.106)= Φ

n

X

i=1

sijbi

!

=

n

X

i=1

sijΦ(bi)=

n

X

i=1

sij m

X

l=1

alicl (2.109a)

=

m

X

l=1 n

X

i=1

alisij

!

cl, j= 1, . . . , n , (2.109b) where we exploited the linearity ofΦ. Comparing (2.108) and (2.109b), it follows for allj= 1, . . . , nandl= 1, . . . , mthat

m

X

k=1

tlk˜akj=

n

X

i=1

alisij (2.110)

and, therefore,

TA˜Φ=AΦS ∈R^m×n, (2.111) such that

A˜Φ=T⁻¹AΦS, (2.112)

which proves Theorem 2.20.

Theorem 2.20 tells us that with a basis change inV (Bis replaced with B˜) and W (C is replaced with C˜), the transformation matrixAΦ of a linear mappingΦ :V →W is replaced by an equivalent matrixA˜Φwith

A˜Φ=T⁻¹AΦS. (2.113)

Figure 2.11 illustrates this relation: Consider a homomorphismΦ :V → W and ordered basesB,B˜ ofV andC,C˜ ofW. The mappingΦCB is an instantiation ofΦand maps basis vectors of B onto linear combinations of basis vectors ofC. Assume that we know the transformation matrixAΦ

ofΦCB with respect to the ordered basesB, C. When we perform a basis change fromB toB˜ inV and from C toC˜ in W, we can determine the

Figure 2.11 For a homomorphism Φ :V →Wand ordered basesB,B˜ ofV andC,C˜ofW (marked in blue), we can express the mappingΦC˜B˜ with respect to the bases B,˜ C˜equivalently as a composition of the homomorphisms ΦC˜B˜ =

ΞCC˜ ◦ΦCB◦Ψ_BB˜

with respect to the bases in the subscripts. The corresponding transformation matrices are in red.

V W

B

B˜ C˜

C Φ ΦCB

ΦC˜B˜

ΨBB˜ S T ΞCC˜

A˜Φ

AΦ

V W

B

B˜ C˜

C Φ ΦCB

ΦC˜B˜

ΨBB˜ S T⁻¹ ΞCC˜ = Ξ⁻¹_CC˜

A˜Φ

AΦ

Vector spaces

Ordered bases

corresponding transformation matrixA˜Φas follows: First, we find the ma- trix representation of the linear mappingΨ_BB˜ :V →V that maps coordi- nates with respect to the new basisB˜ onto the (unique) coordinates with respect to the “old” basisB (inV). Then, we use the transformation ma- trixAΦofΦCB :V →W to map these coordinates onto the coordinates with respect toC inW. Finally, we use a linear mappingΞCC˜ :W →W to map the coordinates with respect toConto coordinates with respect to C˜. Therefore, we can express the linear mappingΦC˜B˜ as a composition of linear mappings that involve the “old” basis:

ΦC˜B˜ = ΞCC˜ ◦ΦCB◦Ψ_BB˜ = Ξ⁻¹_CC˜◦ΦCB◦Ψ_BB˜. (2.114) Concretely, we useΨ_BB˜ = idV andΞ_CC˜ = idW, i.e., the identity mappings that map vectors onto themselves, but with respect to a different basis.

Definition 2.21(Equivalence). Two matricesA,A˜ ∈R^m×nareequivalent

equivalent

if there exist regular matrices S ∈ ^{Rn×n and} T ∈ ^Rm×m, such that A˜ =T⁻¹AS.

Definition 2.22(Similarity). Two matrices A,A˜ ∈ R^n×n are similar if

similar

there exists a regular matrixS∈R^n×nwithA˜ =S⁻¹AS

Remark. Similar matrices are always equivalent. However, equivalent ma-

trices are not necessarily similar. ♢

Remark. Consider vector spacesV, W, X. From the remark that follows Theorem 2.17, we already know that for linear mappingsΦ : V → W and Ψ : W → X the mapping Ψ◦ Φ : V → X is also linear. With transformation matricesAΦandAΨ of the corresponding mappings, the overall transformation matrix isAΨ◦Φ=AΨAΦ. ♢ In light of this remark, we can look at basis changes from the perspec- tive of composing linear mappings:

AΦ is the transformation matrix of a linear mapping ΦCB : V → W with respect to the basesB, C.

A˜Φis the transformation matrix of the linear mappingΦC˜B˜ :V → W with respect to the basesB,˜ C˜.

S is the transformation matrix of a linear mapping Ψ_BB˜ : V → V (automorphism) that representsB˜in terms ofB. Normally,Ψ = idV is the identity mapping inV.

2.7 Linear Mappings 57 T is the transformation matrix of a linear mapping Ξ_CC˜ : W → W (automorphism) that representsC˜in terms ofC. Normally,Ξ = idW is the identity mapping inW.

If we (informally) write down the transformations just in terms of bases, then AΦ : B → C, A˜Φ : ˜B → C˜, S : ˜B → B, T : ˜C → C and T⁻¹ :C→C˜, and

B˜ →C˜=B˜ →B→C→C˜ (2.115)

A˜Φ=T⁻¹AΦS. (2.116)

Note that the execution order in (2.116) is from right to left because vec- tors are multiplied at the right-hand side so thatx7→Sx7→ AΦ(Sx) 7→

T⁻¹ AΦ(Sx)= ˜AΦx. Example 2.24 (Basis Change)

Consider a linear mappingΦ :R³→^R4whose transformation matrix is

AΦ=









1 2 0

−1 1 3 3 7 1

−1 2 4









(2.117)

with respect to the standard bases

B = (



 1 0 0



,



 0 1 0



,



 0 0 1



), C = (







 1 0 0 0







 ,







 0 1 0 0







 ,







 0 0 1 0







 ,







 0 0 0 1









). (2.118)

We seek the transformation matrixA˜ΦofΦwith respect to the new bases

B˜ = (



 1 1 0



,



 0 1 1



,



 1 0 1



)∈R³, C˜= (







 1 1 0 0







 ,







 1 0 1 0







 ,







 0 1 1 0







 ,







 1 0 0 1









). (2.119)

Then,

S =





1 0 1 1 1 0 0 1 1



, T =









1 1 0 1 1 0 1 0 0 1 1 0 0 0 0 1









, (2.120)

where the ith column of S is the coordinate representation of ˜bi in terms of the basis vectors of B. Since B is the standard basis, the co- ordinate representation is straightforward to find. For a general basis B, we would need to solve a linear equation system to find theλi such that

P3

i=1λibi = ˜bj,j= 1, . . . ,3. Similarly, thejth column ofT is the coordi- nate representation of˜cjin terms of the basis vectors ofC.

Therefore, we obtain

A˜Φ=T⁻¹AΦS= 1 2









1 1 −1 −1 1 −1 1 −1

−1 1 1 1

0 0 0 2

















3 2 1 0 4 2 10 8 4 1 6 3









(2.121a)

=









−4 −4 −2

6 0 0

4 8 4

1 6 3









. (2.121b)

In Chapter 4, we will be able to exploit the concept of a basis change to find a basis with respect to which the transformation matrix of an en- domorphism has a particularly simple (diagonal) form. In Chapter 10, we will look at a data compression problem and find a convenient basis onto which we can project the data while minimizing the compression loss.

2.7.3 Image and Kernel

The image and kernel of a linear mapping are vector subspaces with cer- tain important properties. In the following, we will characterize them more carefully.

Definition 2.23(Image and Kernel).

ForΦ :V →W, we define thekernel/null space

kernel null space

ker(Φ) := Φ⁻¹(0W) ={v∈V : Φ(v) =0W} ^(2.122) and theimage/range

image range

Im(Φ) := Φ(V) ={w∈W|∃v∈V : Φ(v) =w}. (2.123) We also callV andW also thedomainandcodomainofΦ, respectively.

domain codomain

Intuitively, the kernel is the set of vectorsv ∈V thatΦmaps onto the neutral element0W ∈ W. The image is the set of vectorsw ∈ W that can be “reached” byΦ from any vector in V. An illustration is given in Figure 2.12.

Remark. Consider a linear mappingΦ : V → W, whereV, W are vector spaces.

It always holds that Φ(0V) = 0W and, therefore, 0V ∈ ker(Φ). In particular, the null space is never empty.

Im(Φ)⊆W is a subspace ofW, andker(Φ)⊆V is a subspace ofV.

2.7 Linear Mappings 59

Figure 2.12 Kernel and image of a linear mapping Φ :V →W.

Im(Φ) 0W

ker(Φ) 0V

Φ :V →W

V W

Φis injective (one-to-one) if and only ifker(Φ) ={0}^.

♢ Remark(Null Space and Column Space). Let us considerA∈R^m×nand a linear mappingΦ :Rⁿ →R^m,x7→Ax.

ForA= [a1, . . . ,an], whereai are the columns ofA, we obtain Im(Φ) ={Ax:x∈Rⁿ}=

( _n X

i=1

xiai:x1, . . . , xn ∈R )

(2.124a)

= span[a1, . . . ,an]⊆R^m, (2.124b)

i.e., the image is the span of the columns of A, also called thecolumn column space

space. Therefore, the column space (image) is a subspace ofR^m, where mis the “height” of the matrix.

rk(A) = dim(Im(Φ)).

The kernel/null space ker(Φ) is the general solution to the homoge- neous system of linear equations Ax = 0 and captures all possible linear combinations of the elements inRⁿthat produce0∈R^m. The kernel is a subspace ofRⁿ, wherenis the “width” of the matrix.

The kernel focuses on the relationship among the columns, and we can use it to determine whether/how we can express a column as a linear combination of other columns.

♢

Example 2.25 (Image and Kernel of a Linear Mapping) The mapping

Φ :R⁴→R²,







 x1

x2

x3

x4







7→

1 2 −1 0 1 0 0 1







 x1

x2

x3

x4









=

x1+ 2x2−x3

x1+x4

(2.125a)

=x1

1 1

+x2

2 0

+x3

−1 0

+x4

0 1

(2.125b) is linear. To determineIm(Φ), we can take the span of the columns of the transformation matrix and obtain

Im(Φ) = span[

1 1

, 2

0

, −1

0

, 0

1

]. (2.126)

To compute the kernel (null space) ofΦ, we need to solveAx= 0, i.e., we need to solve a homogeneous equation system. To do this, we use Gaussian elimination to transformAinto reduced row-echelon form:

1 2 −1 0 1 0 0 1

⇝· · ·⇝

1 0 0 1 0 1 −¹₂ −¹₂

. (2.127)

This matrix is in reduced row-echelon form, and we can use the Minus- 1 Trick to compute a basis of the kernel (see Section 2.3.3). Alternatively, we can express the non-pivot columns (columns3 and4) as linear com- binations of the pivot columns (columns1and2). The third columna3is equivalent to−¹2times the second columna2. Therefore,0=a3+¹₂a2. In the same way, we see thata4=a1−¹2a2and, therefore,0=a1−¹2a2−a4. Overall, this gives us the kernel (null space) as

ker(Φ) = span[







 0

1 2

1 0







 ,









−1

1 2

0 1









]. (2.128)

rank-nullity

theorem Theorem 2.24(Rank-Nullity Theorem). For vector spacesV, W and a lin- ear mappingΦ :V →W it holds that

dim(ker(Φ)) + dim(Im(Φ)) = dim(V). (2.129) The rank-nullity theorem is also referred to as thefundamental theorem

fundamental theorem of linear

mappings of linear mappings(Axler, 2015, theorem 3.22). The following are direct consequences of Theorem 2.24:

If dim(Im(Φ)) < dim(V), then ker(Φ) is non-trivial, i.e., the kernel contains more than0V anddim(ker(Φ))⩾1.

IfAΦis the transformation matrix ofΦwith respect to an ordered basis anddim(Im(Φ))<dim(V), then the system of linear equationsAΦx= 0has infinitely many solutions.

Ifdim(V) = dim(W), then the following three-way equivalence holds:

– Φis injective – Φis surjective – Φis bijective sinceIm(Φ)⊆W.

2.8 Affine Spaces 61 2.8 Affine Spaces

In the following, we will have a closer look at spaces that are offset from the origin, i.e., spaces that are no longer vector subspaces. Moreover, we will briefly discuss properties of mappings between these affine spaces, which resemble linear mappings.

Remark. In the machine learning literature, the distinction between linear and affine is sometimes not clear so that we can find references to affine

spaces/mappings as linear spaces/mappings. ♢

2.8.1 Affine Subspaces

Definition 2.25(Affine Subspace). LetV be a vector space,x0 ∈V and U ⊆V a subspace. Then the subset

L=x0+U :={x0+u:u∈U} ^(2.130a)

={v ∈V|∃u∈U :v=x0+u} ⊆V (2.130b)

is called affine subspaceor linear manifoldof V. U is called direction or affine subspace linear manifold direction

direction space, andx0 is calledsupport point. In Chapter 12, we refer to

direction space support point

such a subspace as ahyperplane.

hyperplane

Note that the definition of an affine subspace excludes 0 if x0 ∈/ U. Therefore, an affine subspace is not a (linear) subspace (vector subspace) ofV forx0∈/ U.

Examples of affine subspaces are points, lines, and planes inR³, which do not (necessarily) go through the origin.

Remark. Consider two affine subspacesL=x0+U andL˜ =x˜0+ ˜U of a vector spaceV. Then,L⊆L˜if and only ifU ⊆U˜ andx0−x˜0 ∈U˜.

Affine subspaces are often described byparameters: Consider ak-dimen- sional affine spaceL=x0+U ofV. If(b1, . . . ,bk)is an ordered basis of U, then every elementx∈Lcan be uniquely described as

x=x0+λ1b1+. . .+λkbk, (2.131)

where λ1, . . . , λk ∈ R. This representation is called parametric equation parametric equation

ofLwith directional vectorsb1, . . . ,bk andparametersλ1, . . . , λk. ♢ ^parameters Example 2.26 (Affine Subspaces)

One-dimensional affine subspaces are called lines and can be written line

as y = x0 +λb1, where λ ∈ R and U = span[b1] ⊆ Rⁿ is a one- dimensional subspace ofRⁿ. This means that a line is defined by a sup- port pointx0and a vectorb1that defines the direction. See Figure 2.13 for an illustration.

Two-dimensional affine subspaces of Rⁿ are called planes. The para-

plane

metric equation for planes isy =x0+λ1b1+λ2b2, whereλ1, λ2 ∈R andU = span[b1,b2] ⊆ ^Rn. This means that a plane is defined by a support pointx0and two linearly independent vectorsb1,b2that span the direction space.

InRⁿ, the(n−1)-dimensional affine subspaces are calledhyperplanes,

hyperplane

and the corresponding parametric equation is y = x0 +^Pn−1_i=1 λibi, where b1, . . . ,bn−1 form a basis of an (n−1)-dimensional subspace U of Rⁿ. This means that a hyperplane is defined by a support point x0and(n−1)linearly independent vectorsb1, . . . ,bn−1 that span the direction space. InR², a line is also a hyperplane. InR³, a plane is also a hyperplane.

Figure 2.13 Lines are affine subspaces.

Vectorsyon a line x0+λb1lie in an affine subspaceL with support point x0and directionb1.

0 x0

b1

y L=x⁰+λb1

Remark(Inhomogeneous systems of linear equations and affine subspaces).

ForA ∈ R^m×n andx ∈ R^m, the solution of the system of linear equa- tions Aλ = x is either the empty set or an affine subspace of Rⁿ of dimension n−rk(A). In particular, the solution of the linear equation λ1b1+. . .+λnbn =x, where(λ1, . . . , λn)̸= (0, . . . ,0), is a hyperplane inRⁿ.

InRⁿ, everyk-dimensional affine subspace is the solution of an inho- mogeneous system of linear equationsAx = b, where A ∈ R^m×n,b ∈ R^m andrk(A) = n−k. Recall that for homogeneous equation systems Ax =0 the solution was a vector subspace, which we can also think of as a special affine space with support pointx0 =0. ♢

2.8.2 Affine Mappings

Similar to linear mappings between vector spaces, which we discussed in Section 2.7, we can define affine mappings between two affine spaces.

Linear and affine mappings are closely related. Therefore, many properties that we already know from linear mappings, e.g., that the composition of linear mappings is a linear mapping, also hold for affine mappings.

Definition 2.26 (Affine Mapping). For two vector spacesV, W, a linear