How statement and proof provenance work
The first chip identifies the source of the statement or construction; the second identifies the source of its local proof or verification.
- Literature-sourced — the exact statement appears in a cited source; only wording and notation differ.
- AI-adapted — a semantically identical restatement of literature-sourced material, modulo indexing, notation, and boundary cases adopted by the library.
- AI-generated — a genuinely novel statement formulated by AI, with no source for the claim itself.
These labels describe origin, not correctness: citations and verification chips remain separate evidence.
Vector space over a field
Definition
Let be a field (Field), with additive identity , multiplicative identity , and the field axioms as stated there. A vector space over , also called an -vector space, consists of
- a set , whose elements are called vectors;
- a binary operation on (Binary operation on a set; associativity, commutativity, and a subset closed under the operation), the vector addition;
- an element , the zero vector;
- a map , the scalar multiplication, written ;
subject to the following axioms, in which and are arbitrary.
- (V1) is an abelian group (Group and abelian group): is associative and commutative, is a two-sided identity for (Left identity, right identity, and two-sided identity for a binary operation), and every has an additive inverse (Left inverse, right inverse, and invertible element of a monoid).
- (V2) .
- (V3) .
- (V4) .
- (V5) .
The elements of are called scalars. When several vector spaces are in play we write for the zero of , and we write for the additive inverse of and .
The notation and is legitimate. Axiom (V1) asserts only that some two-sided identity and some additive inverses exist. That there is at most one two-sided identity for is A left identity and a right identity for the same binary operation are equal; hence there is at most one two-sided identity, and that an invertible element of a monoid has exactly one inverse is In a monoid, a left inverse and a right inverse of the same element are equal; hence an invertible element has exactly one inverse, and it is two-sided; both are proved before Group and abelian group and are inherited here with the group structure. So and denote well-defined elements, and nothing below re-derives them.
What (V1) buys, and why it is not restated. Associativity, commutativity, the identity law , the inverse law , cancellation (Cancellation in a group: or forces ; equivalently left and right translation by are bijections of , so and each have exactly one solution) and the inverse identities (In a group , and , the order of the last product being essential) are facts about abelian groups. They are quoted from the group page wherever they are used and are never proved again for vectors.
Remarks
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Scalar multiplication is not a binary operation on a set. It is a map with arguments from two different sets, so Binary operation on a set; associativity, commutativity, and a subset closed under the operation, which is about a map , does not apply to it and is never cited for it. The definition above cites that item for the vector addition only. In particular "closed under scalar multiplication" below always means for and , which is not an instance of the closure condition defined there.
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(V5) is an axiom, not a consequence of (V2)–(V4). Take any abelian group and define for every and . Then (V2), (V3) and (V4) all hold, both sides of each being , while (V5) fails as soon as . So (V5) has to be imposed, and it is what ties the scalar action to the identity of .
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Two structures, one set. A vector space is data: the set , the addition, the zero, and the scalar multiplication, over a fixed field . The same set may carry vector-space structures over different fields, and the field is part of the statement of every result below. A field is a vector space over itself, and over any subfield every -vector space is a -vector space by restricting the scalars is the first place where that matters.
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The field is the published one. No field axiom is restated here; is a field in the sense of Field, whose axiom (A) already says that is an abelian group and whose axiom (M) says the same of . In particular every field is a vector space over itself, which is A field is a vector space over itself, and over any subfield every -vector space is a -vector space by restricting the scalars.
Depends on
- Field
- Group and abelian group
- Binary operation on a set; associativity, commutativity, and a subset closed under the operation
- Left identity, right identity, and two-sided identity for a binary operation
- Left inverse, right inverse, and invertible element of a monoid
- A left identity and a right identity for the same binary operation are equal; hence there is at most one two-sided identity
- In a monoid, a left inverse and a right inverse of the same element are equal; hence an invertible element has exactly one inverse, and it is two-sided
Used by
- Every linear subspace U of a vector space V has a complement: a linear subspace W with V = U ⊕ W Corollary
- Every spanning subset of a vector space contains a basis Corollary
- Every vector space has a basis Corollary
- If V = bigoplus_i<n Uᵢ with every Uᵢ finite-dimensional, then V is finite-dimensional and dim_F V = ∑_i<n dim_F Uᵢ; in particular dim_F(U ⊕ W) = dim_F U + dim_F W Corollary
- If V has a spanning set with n elements, then every linearly independent subset of V is finite with at most n elements; in particular V has no linearly independent subset equinumerous with ℕ Corollary
- {(1,0), (0,1), (1,1)} spans F² and is linearly dependent, so a spanning set need not be a basis; each of its three two-element subsets is a basis Counterexample
- A discontinuous positive solution of F(x+y)=F(x)F(y) Counterexample
- Inside the space of eventually zero families, the linear subspace spanned by { eᵢ : i ≥ 1 } is proper and has a basis equinumerous with a basis of the whole space, so "equal dimension forces equality" fails without finite dimension Counterexample
- The first quadrant of ℝ² contains 0 and is closed under addition and is not a linear subspace, since it is not closed under multiplication by -1 Counterexample
- The standard unit families { eᵢ : i ∈ ℕ } are linearly independent in F^ℕ but do not span it: the constant family 1_F is not a finite linear combination of them Counterexample
- The union of the two coordinate axes of F² is closed under scalar multiplication and is not closed under addition, so neither closure condition implies the other Counterexample
- Three distinct lines U₀, U₁, U₂ in F² have dim_F(U₀+U₁+U₂) = 2 while the inclusion-exclusion analogue of the dimension formula predicts 3, so the two-subspace formula does not extend Counterexample
- Three lines in F² that meet pairwise only in 0 and whose sum is F² with decompositions that are not unique, so pairwise trivial intersection does not give a direct sum Counterexample
- A norm on a real vector space, the induced metric, and the dictionary with the metric axioms Definition
- Affine subspaces as translates x+U of linear subspaces Definition
- Basis of a vector space: a linearly independent spanning subset; and ordered basis: an injective finite list whose image is a basis Definition
- Equivalent norms, and the dictionary with equivalent metrics Definition
- Finite-dimensional vector space, and its dimension dim_F V; infinite-dimensional means having no finite basis Definition
- Internal direct sum V = bigoplus_i<n Uᵢ: the sum is everything and each summand meets the sum of the others only in 0_V Definition
- Linear combination of a finite list, and the span span(S) as the smallest linear subspace containing S Definition
- Linear independence: a finite list v : n → V is independent when ∑_i<n λᵢ vᵢ = 0_V forces every λᵢ = 0_F, and a subset S ⊆ V is independent when every injective finite list into S is independent Definition
- Linear map between vector spaces over the same field Definition
- Linear subspace of a vector space Definition
- Matrix equation Ax=b, its solution set, consistency, homogeneous systems and the augmented matrix [A∣ b] Definition
- Polygonal paths and polygonally connected subsets of ℝⁿ Definition
- Series of vectors in ℝⁿ, absolute convergence, rearrangement, and the set of rearrangement sums Definition
- The Euclidean inner product ⟨ x,y⟩ = ∑_k<n xₖ yₖ on ℝⁿ Definition
- The space mathcal L(V,W) of linear maps with pointwise addition and scalar multiplication Definition
- The subspace Γ of directions along which a series converges absolutely, and its orthogonal complement Γ^⊥ Definition
- The sum U + W of two linear subspaces and the sum ∑_i<n Uᵢ of a finite family Definition
- The vector space F^X of all functions X → F with pointwise operations, and Fⁿ as the case X = n = {0, 1, …, n-1} Definition
- The vector space M_m × n(F) := F^ m × n of m by n matrices over a field, with entrywise operations Definition
- An additive f : ℝ → ℝ that is not x ↦ cx: the coefficient of one fixed Hamel basis vector. It is unbounded above and below on every nondegenerate interval, its graph is dense in ℝ², and every nonempty level set is dense in ℝ Example
- Every convex subset of ℝⁿ, in particular every ball and ℝⁿ itself, is path-connected and hence connected Example
- F^ℕ is a vector space and the eventually zero families form a linear subspace of it that is the span of the standard unit families Example
- In F³ the three coordinate lines are linear subspaces whose internal direct sum is F³, and F⁰ is the zero space Example
- ℝ as a vector space over ℚ has a basis, and every such basis is infinite; the existence proof exhibits none Example
- ℝ is a vector space over itself, over the embedded copy of ℚ by restriction of scalars, and over ℚ itself via the embedding Example
- The standard unit families eₖ ∈ F^ℕ form a basis of the linear subspace of eventually zero families: an explicit infinite basis, built with no choice principle Example
- The vector (1,2) ∈ ℝ² has coordinate list (1,2) in the standard ordered basis, (2,1) in its reversal, and (2,-1) in the ordered basis ((1,1),(1,0)) Example
…and 34 more results.
Dependency tree · next 3 levels
Direct dependencies and their dependencies through the next three levels: 18 results over 14 levels. An arrow runs from a result to what uses it, and this result sits at the bottom with a heavier outline. Click the chart to enlarge it.
Sources
- Vector space (Wikipedia) (standard reference, not scraped)
- S. Axler, Linear Algebra Done Right, 4th ed., Ch. 1 (standard reference, not scraped)