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.
Finite-dimensional vector space, and its dimension ; infinite-dimensional means having no finite basis
Definition
Let be a vector space over a field (Vector space over a field).
is finite-dimensional over when it has a finite basis (Basis of a vector space: a linearly independent spanning subset; and ordered basis: an injective finite list whose image is a basis, Finite, countably infinite, countable, uncountable): some basis of satisfies for some (Equinumerous sets, and ).
For such a , the dimension of over , written , is that :
This is well defined. Existence of such an is the hypothesis, together
with the fact that a finite set is equinumerous with exactly one natural number
(The pigeonhole principle on , claim 3). Uniqueness is
If has a basis with elements and a basis with elements then ; and if has one finite basis then every basis of is finite: two bases of with and
with elements force . That theorem is therefore a prerequisite of
this definition, not a later justification of it, and it is listed in deps.
is infinite-dimensional over when it is not finite-dimensional over , that is, when has no finite basis. No number is attached to such a space here: the symbol is defined only in the finite-dimensional case, and the expression is not used.
The zero space. is a basis of (Basis of a vector space: a linearly independent spanning subset; and ordered basis: an injective finite list whose image is a basis) and , so is finite-dimensional with . Conversely a space of dimension has a basis , that is , and then (Linear combination of a finite list, and the span as the smallest linear subspace containing ).
Remarks
-
The subscript is not ornamental. By A field is a vector space over itself, and over any subfield every -vector space is a -vector space by restricting the scalars the same set with the same addition is a vector space over any subfield (Subfield: a subring of a field closed under inverses of its nonzero elements, and therefore a field with the restricted operations), and for a proper subfield the two structures can have different bases and different dimensions. The companion page's basis of over is the extreme case: is a vector space both over itself and over the embedded copy of inside it, and it is infinite-dimensional over the latter. So "the dimension of " is incomplete language in exactly the way that "the vector space " is, and both the space and the field are part of the statement of every result below.
-
Infinite-dimensional is defined as a negation, deliberately. Assigning a size to an infinite basis would require knowing that any two infinite bases of a space are equinumerous, which If has a basis with elements and a basis with elements then ; and if has one finite basis then every basis of is finite does not prove and this page does not claim; the standard argument for it is cardinal arithmetic, developed much later in the library. The companion page therefore records a proper subspace with an equinumerous basis rather than any statement of the form for infinite-dimensional spaces.
-
Dimension counts a basis, not a spanning set and not an independent set. A spanning set may be larger than and an independent set smaller; 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 is what bounds the second by the first, and For the following are equivalent: is a basis; is a maximal linearly independent subset of ; is a minimal spanning subset of — maximality and minimality being in the inclusion order is what says a basis is exactly where the two meet.
Depends on
- If $V$ has a basis with $n$ elements and a basis with $m$ elements then $n = m$; and if $V$ has one finite basis then every basis of $V$ is finite
- Basis of a vector space: a linearly independent spanning subset; and ordered basis: an injective finite list whose image is a basis
- Linear independence: a finite list $v : n \to V$ is independent when $\sum_{i<n} \lambda_i v_i = 0_V$ forces every $\lambda_i = 0_F$, and a subset $S \subseteq V$ is independent when every injective finite list into $S$ is independent
- Linear combination of a finite list, and the span $\operatorname{span}(S)$ as the smallest linear subspace containing $S$
- A field is a vector space over itself, and over any subfield $K \subseteq F$ every $F$-vector space is a $K$-vector space by restricting the scalars
- Subfield: a subring of a field closed under inverses of its nonzero elements, and therefore a field with the restricted operations
- Vector space over a field
- Field
- Finite, countably infinite, countable, uncountable
- Equinumerous sets, $A \approx B$ and $A \preceq B$
- Injection, surjection, bijection
- The pigeonhole principle on $\mathbb{N}$
- The natural numbers $\mathbb{N}$ (von Neumann)
Used by
- dim_F M_m× n(F)=mn and dim_F mathcal L(V,W)=(dim_FV)(dim_FW) for finite-dimensional V,W 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
- Two finite-dimensional vector spaces over F are linearly isomorphic if and only if they have the same dimension 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
- 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 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
- 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
- Rank and nullity of a linear map with finite-dimensional domain Definition
- Row space, column space, nullspace, row rank, column rank and matrix rank Definition
- The basis-independent trace of an endomorphism of a finite-dimensional vector space Definition
- ℝ as a vector space over ℚ has a basis, and every such basis is infinite; the existence proof exhibits none 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
- FALSE: all norms on a real vector space are equivalent False statement
- Extending a basis of the kernel to a basis of the domain gives a basis of the image Lemma
- The standard list e : n → Fⁿ with eᵢ(i) = 1_F and eᵢ(j) = 0_F for j ≠ i is an ordered basis of Fⁿ; hence dim_F Fⁿ = n, and F⁰ is the zero space with basis ∅ and dimension 0 Lemma
- A square matrix is invertible exactly when its multiplication map is a linear isomorphism; matrices preserve inverses of linear isomorphisms Theorem
- If dim_F V = n and U is a linear subspace of V, then U is finite-dimensional, dim_F U ≤ n, and dim_F U = n if and only if U = V Theorem
- Rank-nullity: dim_F V=nullityT+rankT Theorem
- Steinitz's polygonal confinement theorem: finitely many vectors of norm at most 1 summing to 0 can be ordered so that every partial sum has norm at most n Theorem
- The dimension formula: for finite-dimensional linear subspaces U and W of V, the subspaces U + W and U ∩ W are finite-dimensional and dim_F(U+W) + dim_F(U ∩ W) = dim_F U + dim_F W Theorem
Dependency tree · next 3 levels
Direct dependencies and their dependencies through the next three levels: 86 results over 26 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
- Dimension (vector space) (Wikipedia) (standard reference, not scraped)
- S. Axler, Linear Algebra Done Right, 4th ed., Ch. 2 (standard reference, not scraped)
- Interactive Linear Algebra: Dimension (standard reference, not scraped)
- Sheldon Axler, Linear Algebra Done Right, 4th ed. (standard reference, not scraped)