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Linear Transformations, Rank-Nullity and Quotient Spaces
1 · Prerequisites
- Binary Operations, Monoids, Groups and Subgroups
- Construction of the Natural Numbers
- Construction of the Real Numbers via Cauchy Sequences
- Construction of the Real Numbers via Dedekind Cuts
- Countability and Uncountability
- Finite Counting, Factorials and Binomial Coefficients
- Foundations of the Real Numbers for Analysis
- Linear Independence, Bases and Dimension
- Relations, Functions, and Quotients
- Rings, Subrings, Integral Domains and Fields
- Roots, Rational Powers, and Classical Inequalities
- The ZFC Axioms and the Basic Set Constructions
- Vector Spaces, Linear Subspaces, Span and Direct Sums
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
Linear map between vector spaces over the same field
Definition
Let and be vector spaces over the same field . A function is linear when
for all and . Such a function is a linear map, or linear transformation.
A linear map preserves zero, negatives, and subtraction
Statement
If is linear, then , , and for all .
Facts & Assumptions
Given: Vector spaces over a field and a linear map .
A linear map satisfies (Linear map between vector spaces over the same field).
The additive structure of a vector space is an abelian group, so it has identities, inverses, cancellation, and ; also and (Vector space over a field, In any vector space , , , , and forces or ).
Proof
Linearity gives ; cancellation in gives .
Since , the inverse law gives .
Using and step 2.1, .
Kernel and image of a linear map
Definition
For a linear map , its kernel and image are respectively
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 ↗.
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 , the sets and are linear subspaces. Moreover, is injective if and only if .
Facts & Assumptions
Given: A linear map of vector spaces over a field .
The kernel and image have the displayed set descriptions (Kernel and image of a linear map).
A nonempty subset is a linear subspace exactly when it is closed under (One-step subspace test: a nonempty is a linear subspace if and only if for all and ).
A linear map carries to , preserves zero, and preserves subtraction (Linear map between vector spaces over the same field, A linear map preserves zero, negatives, and subtraction).
A function is injective when equal outputs have equal inputs (Injection, surjection, bijection).
Proof
The kernel contains by [L3]. If and , then , so .
The image contains . If and , then lies in the image.
If is injective and , then , so and the kernel is trivial.
Conversely, suppose and . Then by [L3], so and .
The subspace criterion proves that both and are linear subspaces.
Thus is injective exactly when its kernel is trivial.
Extending a basis of the kernel to a basis of the domain gives a basis of the image
Statement
Let be linear, with finite-dimensional over . There are a basis of and a basis of with . For this pair, put . Then is finite, the restriction
is a bijection, and is a basis of .
More explicitly, if and are bijections, then the list obtained by placing before is an ordered basis of , while is an ordered basis of the image.
Facts & Assumptions
Given: Vector spaces over , a linear map , and a finite-dimensional domain .
The kernel and image of a linear map are linear subspaces, and exactly when (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).
A linear subspace of a finite-dimensional vector space is finite-dimensional; every linearly independent subset extends to a basis without a choice principle; and every linearly independent subset of a finite-dimensional space is finite (If and is a linear subspace of , then is finite-dimensional, , and if and only if , claims 1 and 3, If has a spanning set with elements, then every linearly independent subset of is finite with at most elements; in particular has no linearly independent subset equinumerous with ).
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).
Every vector has exactly one coordinate list with respect to an ordered basis (A finite list is an ordered basis if and only if every equals for exactly one ; those scalars are the coordinates of in that ordered basis).
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 and (The product of a finite list in a monoid, by recursion, with the empty product () equal to the identity, The principle of mathematical induction, Addition of natural numbers).
A linear map satisfies ; vector spaces have the usual zero, inverse, and distributive laws (Linear map between vector spaces over the same field, In any vector space , , , , and forces or ).
A subset of a finite set is finite. If two finite sets are disjoint, then their union has cardinality the sum of their cardinalities; finite cardinality is transported by a bijection (A subset of a finite set is finite, with , and equality holds if and only if , The sum rule: a finite disjoint union is finite with and , and a sum over a finite index set splits along a partition, clause 1, The cardinality of a finite set).
A function is bijective exactly when it is injective and surjective (Injection, surjection, bijection).
Proof
By [L1] and [L2], is finite-dimensional; choose a basis of . It is linearly independent in , so [L2] extends it to a basis of . Put . Since is finite, so is , and .
For every finite list and every scalar list , linearity gives . This follows by induction on : the empty case follows by applying [L6] to and cancelling, and the successor step uses the append recursion and . The same recursion, now by induction on the length of the second list, splits a spliced sum into its first and second blocks.
Write and , and choose bijections and . Splicing the two lists gives , with for and for . The disjointness in step 1.1 makes injective and its image is , so it is an ordered basis of .
The set spans . Indeed, if lies in the image, [L4] gives coordinates with . Steps 2.1 and 1.2 split this into the -block and the -block. Applying kills the first block because every lies in , leaving as a finite linear combination of .
The list is linearly independent. Suppose . By step 1.2, lies in , so [L4] supplies with . Thus the spliced ordered basis has a vanishing linear combination whose coefficients are on the first block and on the second. Its linear independence forces every .
Linear independence makes injective. It is surjective onto by that set's definition, so it is a bijection . Together with steps 3.1 and 3.2, this says that it is an ordered basis of and that is bijective.
Step 1.1 supplies and , and step 4.1 proves all the asserted properties of their complement .
Remarks
- The construction is valid when is the zero space: then , hence , and both empty lists are the ordered bases of the zero spaces.
- No choice principle is used. Both basis selections occur inside finite-dimensional spaces and are licensed by the finite extension clause of If and is a linear subspace of , then is finite-dimensional, , and if and only if .
Rank and nullity of a linear map with finite-dimensional domain
Definition
Let be a linear map whose domain is finite-dimensional over . Its nullity and rank are
Both dimensions are defined. The kernel is a linear subspace of the finite-dimensional space , 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 and is a linear subspace of , then is finite-dimensional, , and if and only if . 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 . Thus rank and nullity are natural numbers, including when is the zero space.
Rank-nullity:
Statement
Let be a linear map of vector spaces over , with finite-dimensional. Then
Equivalently,
Facts & Assumptions
Given: A linear map with finite-dimensional over .
Nullity and rank are the dimensions of the kernel and image (Rank and nullity of a linear map with finite-dimensional domain).
There are finite bases of and of with ; for , the set is finite, is a basis of , and is bijective (Extending a basis of the kernel to a basis of the domain gives a basis of the image).
The dimension of a finite-dimensional vector space is the number of elements in any finite basis (Finite-dimensional vector space, and its dimension ; infinite-dimensional means having no finite basis).
If two finite sets are disjoint, the cardinality of their union is the sum of their cardinalities (The sum rule: a finite disjoint union is finite with and , and a sum over a finite index set splits along a partition, clause 1).
A bijection between finite sets transports their cardinality (The cardinality of a finite set, consequence (c)).
Proof
Choose as in [L2]. Since , [L4] gives . The bijection gives .
Since , , and are bases of , , and , respectively, [L1] and [L3] turn step 1.1 into .
The displayed equivalent form follows by unfolding the definitions of rank and nullity.
Remarks
- If , all three dimensions are zero and the formula reads ; no positive-dimension hypothesis is hidden.
- No finite-dimensionality assumption is made on . The image is finite-dimensional for the reason isolated in Extending a basis of the kernel to a basis of the domain gives a basis of the image.
5 · Examples, counterexamples and false statements
None yet.
Sources
Standard references
Recommended treatments; not extraction sources.