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Linear Transformations, Rank-Nullity and Quotient Spaces

1 · Prerequisites

2 · Summary

Vector spaces, linear combinations, bases, and finite dimension are the prerequisites for this development. A linear map preserves the vector-space operations, and its kernel and image measure respectively what it collapses and what it reaches.

This page defines linear maps, kernels, images, rank, and nullity. It proves the elementary preservation laws, characterises injectivity by the kernel, extends a kernel basis to obtain a basis of the image, and derives the finite-dimensional rank–nullity theorem.

3 · Logical flowchart

4 · Definitions, theorems and proofs

DefinitionDefinition: Literature-sourcedProof: Not applicableaudited 2026-08-03Open item page →

Linear map between vector spaces over the same field

Definition

Let VV and WW be vector spaces over the same field FF. A function T:VWT:V\to W is linear when

T(au+bv)=aT(u)+bT(v)T(au+bv)=aT(u)+bT(v)

for all a,bFa,b\in F and u,vVu,v\in V. Such a function is a linear map, or linear transformation.

LemmaStatement: Literature-sourcedProof: AI-adaptedprecheck passaudited 2026-08-03Open item page →

A linear map preserves zero, negatives, and subtraction

Statement

If T:VWT:V\to W is linear, then T(0V)=0WT(0_V)=0_W, T(v)=T(v)T(-v)=-T(v), and T(uv)=T(u)T(v)T(u-v)=T(u)-T(v) for all u,vVu,v\in V.

Facts & Assumptions

Given: Vector spaces V,WV,W over a field FF and a linear map T:VWT:V\to W.

[L1]

A linear map satisfies T(au+bv)=aT(u)+bT(v)T(au+bv)=aT(u)+bT(v) (Linear map between vector spaces over the same field).

[L2]

The additive structure of a vector space is an abelian group, so it has identities, inverses, cancellation, and uv=u+(v)u-v=u+(-v); also 0Fv=0V0_Fv=0_V and (1F)v=v(-1_F)v=-v (Vector space over a field, In any vector space 0Fv=0V0_F v = 0_V, λ0V=0V\lambda 0_V = 0_V, (λ)v=(λv)(-\lambda)v = -(\lambda v), (1F)v=v(-1_F)v = -v, and λv=0V\lambda v = 0_V forces λ=0F\lambda = 0_F or v=0Vv = 0_V).

Proof

technique · direct
1.1

Linearity gives T(0V)=T(0F0V+0F0V)=T(0V)+T(0V)T(0_V)=T(0_F0_V+0_F0_V)=T(0_V)+T(0_V); cancellation in WW gives T(0V)=0WT(0_V)=0_W.

L1L2given
2.1

Since 0W=T(0V)=T(v+(v))=T(v)+T(v)0_W=T(0_V)=T(v+(-v))=T(v)+T(-v), the inverse law gives T(v)=T(v)T(-v)=-T(v).

step 1.1L1L2given
3.1

Using uv=u+(v)u-v=u+(-v) and step 2.1, T(uv)=T(u)+T(v)=T(u)T(v)T(u-v)=T(u)+T(-v)=T(u)-T(v).

step 2.1L1L2given
DefinitionDefinition: Literature-sourcedProof: Not applicableaudited 2026-08-03Open item page →

Kernel and image of a linear map

Definition

For a linear map T:VWT:V\to W, its kernel and image are respectively

kerT:={vV:T(v)=0W},imT:={T(v):vV}.\ker T:=\{v\in V:T(v)=0_W\},\qquad \operatorname{im}T:=\{T(v):v\in V\}.

That both sets are linear subspaces, and that a trivial kernel characterises injectivity, is proved in The kernel and image are linear subspaces, and a linear map is injective if and only if its kernel is trivial .

TheoremStatement: Literature-sourcedProof: AI-adaptedprecheck passaudited 2026-08-03Open item page →

The kernel and image are linear subspaces, and a linear map is injective if and only if its kernel is trivial

Statement

For a linear map T:VWT:V\to W, the sets kerT\ker T and imT\operatorname{im}T are linear subspaces. Moreover, TT is injective if and only if kerT={0V}\ker T=\{0_V\}.

Facts & Assumptions

Given: A linear map T:VWT:V\to W of vector spaces over a field FF.

[L1]

The kernel and image have the displayed set descriptions (Kernel and image of a linear map).

[L3]

A linear map carries au+vau+v to aT(u)+T(v)aT(u)+T(v), preserves zero, and preserves subtraction (Linear map between vector spaces over the same field, A linear map preserves zero, negatives, and subtraction).

[L4]

A function is injective when equal outputs have equal inputs (Injection, surjection, bijection).

Proof

technique · direct
1.1

The kernel contains 0V0_V by [L3]. If u,vkerTu,v\in\ker T and aFa\in F, then T(au+v)=aT(u)+T(v)=0WT(au+v)=aT(u)+T(v)=0_W, so au+vkerTau+v\in\ker T.

L1L2L3given
1.2

The image contains 0W=T(0V)0_W=T(0_V). If T(u),T(v)imTT(u),T(v)\in\operatorname{im}T and aFa\in F, then aT(u)+T(v)=T(au+v)aT(u)+T(v)=T(au+v) lies in the image.

L1L2L3given
1.3

If TT is injective and vkerTv\in\ker T, then T(v)=0W=T(0V)T(v)=0_W=T(0_V), so v=0Vv=0_V and the kernel is trivial.

L1L3L4given
1.4

Conversely, suppose kerT={0V}\ker T=\{0_V\} and T(u)=T(v)T(u)=T(v). Then T(uv)=0WT(u-v)=0_W by [L3], so uv=0Vu-v=0_V and u=vu=v.

L1L3L4given
2.1

The subspace criterion proves that both kerT\ker T and imT\operatorname{im}T are linear subspaces.

step 1.1step 1.2L2
3.1

Thus TT is injective exactly when its kernel is trivial.

step 1.3step 1.4
LemmaStatement: Literature-sourcedProof: AI-adaptedprecheck passaudited 2026-08-03Open item page →

Extending a basis of the kernel to a basis of the domain gives a basis of the image

Statement

Let T:VWT:V\to W be linear, with VV finite-dimensional over FF. There are a basis KK of kerT\ker T and a basis BB of VV with KBK\subseteq B. For this pair, put C:=BKC:=B\setminus K. Then CC is finite, the restriction

TC:CT[C]T|_C:C\longrightarrow T[C]

is a bijection, and T[C]T[C] is a basis of imT\operatorname{im}T.

More explicitly, if k:νKk:\nu\to K and c:rCc:r\to C are bijections, then the list obtained by placing kk before cc is an ordered basis of VV, while Tc:rimTT\circ c:r\to\operatorname{im}T is an ordered basis of the image.

Facts & Assumptions

Given: Vector spaces V,WV,W over FF, a linear map T:VWT:V\to W, and a finite-dimensional domain VV.

[L1]

The kernel and image of a linear map are linear subspaces, and vkerTv\in\ker T exactly when T(v)=0WT(v)=0_W (The kernel and image are linear subspaces, and a linear map is injective if and only if its kernel is trivial, Kernel and image of a linear map).

[L3]

A basis is a linearly independent spanning subset. An ordered basis is an injective finite list whose image is a basis, equivalently a linearly independent finite list that spans (Basis of a vector space: a linearly independent spanning subset; and ordered basis: an injective finite list whose image is a basis).

[L5]

Finite sums in an abelian group have empty value zero and append one term at a successor; induction is valid on their natural-number length. Natural addition satisfies p+0=pp+0=p and p+σ(q)=σ(p+q)p+\sigma(q)=\sigma(p+q) (The product g0g1gn1g_0 g_1 \cdots g_{n-1} of a finite list in a monoid, by recursion, with the empty product (n=0n = 0) equal to the identity, The principle of mathematical induction, Addition of natural numbers).

[L8]

A function is bijective exactly when it is injective and surjective (Injection, surjection, bijection).

Proof

technique · direct
1.1

By [L1] and [L2], kerT\ker T is finite-dimensional; choose a basis KK of kerT\ker T. It is linearly independent in VV, so [L2] extends it to a basis BB of VV. Put C:=BKC:=B\setminus K. Since BB is finite, so is CC, and B=K˙CB=K\mathbin{\dot\cup}C.

L1L2L7given
1.2

For every finite list v:nVv:n\to V and every scalar list a:nFa:n\to F, linearity gives T(i<naivi)=i<naiT(vi)T(\sum_{i<n}a_iv_i)=\sum_{i<n}a_iT(v_i). This follows by induction on nn: the empty case follows by applying [L6] to 0V=0V+0V0_V=0_V+0_V and cancelling, and the successor step uses the append recursion and T(x+anvn)=T(x)+anT(vn)T(x+a_nv_n)=T(x)+a_nT(v_n). The same recursion, now by induction on the length of the second list, splits a spliced sum into its first and second blocks.

L5L6discharge-induction
2.1

Write ν:=K\nu:=|K| and r:=Cr:=|C|, and choose bijections k:νKk:\nu\to K and c:rCc:r\to C. Splicing the two lists gives d:ν+rBd:\nu+r\to B, with di=kid_i=k_i for i<νi<\nu and dν+j=cjd_{\nu+j}=c_j for j<rj<r. The disjointness in step 1.1 makes dd injective and its image is BB, so it is an ordered basis of VV.

step 1.1L3L5L7L8construct
3.1

The set T[C]T[C] spans imT\operatorname{im}T. Indeed, if y=T(v)y=T(v) lies in the image, [L4] gives coordinates a:ν+rFa:\nu+r\to F with v=i<ν+raidiv=\sum_{i<\nu+r}a_id_i. Steps 2.1 and 1.2 split this into the KK-block and the CC-block. Applying TT kills the first block because every kik_i lies in kerT\ker T, leaving yy as a finite linear combination of T(c0),,T(cr1)T(c_0),\ldots,T(c_{r-1}).

step 2.1step 1.2L1L3L4L6
3.2

The list Tc:rWT\circ c:r\to W is linearly independent. Suppose j<rbjT(cj)=0W\sum_{j<r}b_jT(c_j)=0_W. By step 1.2, u:=j<rbjcju:=\sum_{j<r}b_jc_j lies in kerT\ker T, so [L4] supplies a:νFa:\nu\to F with u=i<νaikiu=\sum_{i<\nu}a_ik_i. Thus the spliced ordered basis dd has a vanishing linear combination whose coefficients are ai-a_i on the first block and bjb_j on the second. Its linear independence forces every bj=0Fb_j=0_F.

step 2.1step 1.2L1L3L4L6
4.1

Linear independence makes TcT\circ c injective. It is surjective onto T[C]T[C] by that set's definition, so it is a bijection rT[C]r\to T[C]. Together with steps 3.1 and 3.2, this says that it is an ordered basis of imT\operatorname{im}T and that TC:CT[C]T|_C:C\to T[C] is bijective.

step 3.1step 3.2L3L8
5.1

Step 1.1 supplies KK and BB, and step 4.1 proves all the asserted properties of their complement CC.

step 1.1step 4.1

Remarks

DefinitionDefinition: Literature-sourcedProof: Not applicableaudited 2026-08-03Open item page →

Rank and nullity of a linear map with finite-dimensional domain

Definition

Let T:VWT:V\to W be a linear map whose domain VV is finite-dimensional over FF. Its nullity and rank are

nullityT:=dimF(kerT),rankT:=dimF(imT).\operatorname{nullity}T:=\dim_F(\ker T),\qquad \operatorname{rank}T:=\dim_F(\operatorname{im}T).

Both dimensions are defined. The kernel is a linear subspace of the finite-dimensional space VV, so it is finite-dimensional by The kernel and image are linear subspaces, and a linear map is injective if and only if its kernel is trivial and If dimFV=n\dim_F V = n and UU is a linear subspace of VV, then UU is finite-dimensional, dimFUn\dim_F U \le n, and dimFU=n\dim_F U = n if and only if U=VU = V. The image is finite-dimensional because Extending a basis of the kernel to a basis of the domain gives a basis of the image constructs a finite basis of it from a basis of VV. Thus rank and nullity are natural numbers, including when VV is the zero space.

TheoremStatement: Literature-sourcedProof: AI-adaptedprecheck passaudited 2026-08-03Open item page →

Rank-nullity: dimFV=nullityT+rankT\dim_F V=\operatorname{nullity}T+\operatorname{rank}T

Statement

Let T:VWT:V\to W be a linear map of vector spaces over FF, with VV finite-dimensional. Then

dimFV=nullityT+rankT.\dim_F V=\operatorname{nullity}T+\operatorname{rank}T.

Equivalently,

dimFV=dimF(kerT)+dimF(imT).\dim_F V=\dim_F(\ker T)+\dim_F(\operatorname{im}T).

Facts & Assumptions

Given: A linear map T:VWT:V\to W with VV finite-dimensional over FF.

[L1]

Nullity and rank are the dimensions of the kernel and image (Rank and nullity of a linear map with finite-dimensional domain).

[L2]

There are finite bases KK of kerT\ker T and BB of VV with KBK\subseteq B; for C=BKC=B\setminus K, the set CC is finite, T[C]T[C] is a basis of imT\operatorname{im}T, and TC:CT[C]T|_C:C\to T[C] is bijective (Extending a basis of the kernel to a basis of the domain gives a basis of the image).

[L3]

The dimension of a finite-dimensional vector space is the number of elements in any finite basis (Finite-dimensional vector space, and its dimension dimFV\dim_F V; infinite-dimensional means having no finite basis).

[L5]

A bijection between finite sets transports their cardinality (The cardinality A\lvert A\rvert of a finite set, consequence (c)).

Proof

technique · direct
1.1

Choose K,B,CK,B,C as in [L2]. Since B=K˙CB=K\mathbin{\dot\cup}C, [L4] gives B=K+C|B|=|K|+|C|. The bijection TC:CT[C]T|_C:C\to T[C] gives C=T[C]|C|=|T[C]|.

L2L4L5given
2.1

Since BB, KK, and T[C]T[C] are bases of VV, kerT\ker T, and imT\operatorname{im}T, respectively, [L1] and [L3] turn step 1.1 into dimFV=nullityT+rankT\dim_F V=\operatorname{nullity}T+\operatorname{rank}T.

step 1.1L1L2L3
3.1

The displayed equivalent form follows by unfolding the definitions of rank and nullity.

step 2.1L1

Remarks

5 · Examples, counterexamples and false statements

None yet.

Sources