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If and is a linear subspace of , then is finite-dimensional, , and if and only if
Statement
Let be a vector space over a field (Vector space over a field) that is finite-dimensional with (Finite-dimensional vector space, and its dimension ; infinite-dimensional means having no finite basis), and let be a linear subspace of (Linear subspace of a vector space). Then
- is finite-dimensional over and ;
- if and only if ;
- Extension, with no choice principle. Every linearly independent is contained in a basis of : there is a basis of with . Claim 1 is the case . Since is itself a linear subspace of (Linear subspace of a vector space), claim 3 applies with in place of , and hence to any finite-dimensional vector space over in place of the pair .
Nothing above uses a choice principle, and claim 3 in particular is the finite-dimensional substitute for the Zorn-based extension theorem stated earlier on this page. In finite dimension the extension terminates on its own, because 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 bounds the size of an independent set and The well-ordering principle then supplies a largest one; no selection is made anywhere.
Finiteness is essential in claim 2. Without it the equality case fails: the companion page exhibits a proper linear subspace of an infinite-dimensional space whose basis is equinumerous with a basis of the whole space.
Facts & Assumptions
Given: A field , a vector space over with , and a linear subspace of .
means has a basis with , and a basis is a linearly independent spanning subset (Finite-dimensional vector space, and its dimension ; infinite-dimensional means having no finite basis, 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 is independent when forces every , and a subset is independent when every injective finite list into is independent, Linear combination of a finite list, and the span as the smallest linear subspace containing ).
If has a spanning subset with elements, then every linearly independent subset of is finite with at most elements (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 ).
For , linear independence computed in and in is the same condition, and ; so is a basis of exactly when is linearly independent and (Basis of a vector space: a linearly independent spanning subset; and ordered basis: an injective finite list whose image is a basis, section on bases of a linear subspace, Linear subspace of a vector space).
If is linearly independent and , then and is linearly independent (If is linearly independent and then is linearly independent and ; and if then , claim 2).
is a linear subspace of containing and contained in every linear subspace of containing (Linear combination of a finite list, and the span as the smallest linear subspace containing , The span is monotone and idempotent, exactly when is a linear subspace, and ).
Every nonempty subset of has a least element (The well-ordering principle); is a total order; ; every is a successor; and with ( is a linear order on , Order on the natural numbers, On the order is membership: , Every nonzero natural number is a successor, The natural numbers (von Neumann)).
A finite set is equinumerous with exactly one natural number (The pigeonhole principle on , claim 3); , and means a bijection exists (Equinumerous sets, and , Finite, countably infinite, countable, uncountable, Injection, surjection, bijection).
Proof
Fix a basis of with . It spans and is finite of size , so every linearly independent subset of is finite with at most elements.
A subset is linearly independent as a subset of exactly when it is linearly independent as a subset of , and its span is the same set computed in either space; so "basis of " is unambiguous, and every linearly independent subset of is a linearly independent subset of .
A nonempty with an upper bound has a greatest element. Let be the set of upper bounds of in , nonempty by hypothesis, and let be its least element. If then every satisfies , hence , and is nonempty, so . If , write and suppose ; then every satisfies and , hence , hence , so with , contradicting leastness. Either way , and is an upper bound, so it is the greatest element of .
Fix a linearly independent , possibly empty, and let . Then is nonempty: by step 1.2 the set is a linearly independent subset of , so it is finite by step 1.1, say , and itself witnesses . And every satisfies , since the witnessing is likewise a linearly independent subset of and step 1.1 bounds its size, the size being unique. So is nonempty and bounded by , and step 1.3 gives it a greatest element ; fix a linearly independent with and .
That is a basis of containing . Suppose some had . Then is linearly independent and , and ; moreover a bijection extends to a bijection by sending to , so and , contradicting the maximality of in . Hence ; and because is a linear subspace of containing . So and is a basis of with .
Claim 1. Run steps 2.1 and 3.1 at , which is linearly independent and contained in . They produce a basis of with , so is finite-dimensional with , and by step 2.1.
Claim 3. For an arbitrary linearly independent , steps 2.1 and 3.1 produce a basis of with , which is the assertion. Every selection made along the way is a single existential instantiation from a nonempty set, and the greatest element supplied by step 1.3 is determined by rather than chosen from it, so no choice principle is used. Applying this with in the roles of both and , which is legitimate because is a linear subspace of itself, gives the statement for an arbitrary finite-dimensional vector space over .
Claim 2. If then . Conversely suppose ; then by step 4.1, so the basis produced at in step 3.1 is a linearly independent subset of with and . If , pick ; then is a linearly independent subset of with , and by step 1.1, which is impossible since . So .
Claim 1 is step 4.1, claim 2 is step 5.1 and claim 3 is step 4.2.
Remarks
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No choice principle is used, and claim 3 is why that matters. The basis of is obtained by taking a linearly independent subset of of greatest size among those containing a given , which exists because the sizes form a nonempty set of naturals bounded by (The well-ordering principle); Zorn's lemma is not invoked, and Zorn's lemma gives a basis between any linearly independent set and any spanning set containing it: if with independent and , there is a basis of with is not used here. In finite dimension the existence of bases, and the extension of a given independent set to one, are both free. Claim 3 is the form The dimension formula: for finite-dimensional linear subspaces and of , the subspaces and are finite-dimensional and uses, which is what keeps that theorem choice-free.
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Why the equality case is a real theorem. In finite dimension "same dimension" and "equal" coincide for a subspace and its ambient space, and the proof of that is the observation that a basis of a proper subspace can always be enlarged inside the bigger space. Both halves of the argument fail without finiteness, and the companion page carries the witness.
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The span is the same computed in or in , which is why the statement can mix the two freely. That agreement is proved once, in Basis of a vector space: a linearly independent spanning subset; and ordered basis: an injective finite list whose image is a basis, rather than repeated here; it rests on the fact that a linear subspace carries the addition, the zero and the scalar multiplication of the ambient space (Linear subspace of a vector space).
Depends on
- Finite-dimensional vector space, and its dimension $\dim_F V$; infinite-dimensional means having no finite basis
- 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 $\mathbb{N}$
- If $S \subseteq V$ is linearly independent and $w \notin \operatorname{span}(S)$ then $S \cup \{w\}$ is linearly independent and $\operatorname{span}(S) \subsetneq \operatorname{span}(S \cup \{w\})$; and if $w \in \operatorname{span}(S)$ then $\operatorname{span}(S \cup \{w\}) = \operatorname{span}(S)$
- 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 subspace of a vector space
- Linear combination of a finite list, and the span $\operatorname{span}(S)$ as the smallest linear subspace containing $S$
- The span is monotone and idempotent, $\operatorname{span}(S) = S$ exactly when $S$ is a linear subspace, and $\operatorname{span}(S \cup \{0_V\}) = \operatorname{span}(S)$
- Vector space over a field
- Field
- The well-ordering principle
- 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)
- On $\mathbb{N}$ the order is membership: $m < n \iff m \in n$
- Order on the natural numbers
- $\le$ is a linear order on $\mathbb{N}$
- Every nonzero natural number is a successor
Used by
- An endomorphism of an n-dimensional space has at most n distinct eigenvalues Corollary
- If V = ⨁_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
- In finite dimension, W^⊥⊥=W and dim W+dim W^⊥=dim V Corollary
- 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
- Rank and nullity of a linear map with finite-dimensional domain Definition
- An algebra that is finite dimensional as a vector space over a field is a Noetherian ring Example
- The exponential map of a flat torus is not injective Example
- A convex set and its closure have the same interior and boundary Lemma
- Descended orbit idempotents are primitive and their blocks have one simple type Lemma
- Extending a basis of the kernel to a basis of the domain gives a basis of the image Lemma
- Finite-dimensional subspaces admit projections without Choice Lemma
- For a subspace U≤ Fⁿ, dim_F U^⊥=n-dim_F U, where U^⊥={x:⟨ x,u⟩=0 for all u∈ U} Lemma
- Hopf trace formula Lemma
- If the incidence vectors of A₁,…,Aₘ⊆[n] are linearly independent over F then m≤ n Lemma
- The trace of an idempotent is its rank as a field scalar Lemma
- For a finite-dimensional space, λ is an eigenvalue of T if and only if T-λ I is not invertible Proposition
- A cyclic vector exists exactly when the minimal and characteristic polynomials agree Theorem
- An endomorphism is diagonalisable exactly when V=⨁_i<rE_λᵢ(T) for some finite list of distinct scalars λᵢ Theorem
- Assuming choice, ^∘(U^∘)=U; in finite dimension, dim U^∘=dim V-dim U Theorem
- Atkinson Theorem
- Courant-Fischer min-max principle for self-adjoint endomorphisms on finite-dimensional real inner product spaces Theorem
- Every alternating form on a finite-dimensional space has a basis of symplectic pairs followed by a basis of its radical; in particular its rank is even Theorem
- Every finite-dimensional nilpotent endomorphism has a basis of Jordan strings Theorem
- Every symmetric bilinear form on a finite-dimensional space over a field of characteristic not 2 has an orthogonal basis Theorem
- For a subspace W of a finite-dimensional inner product space, V=W⊕ W^⊥ Theorem
- For an endomorphism in finite dimension, preserving lengths, preserving inner products, carrying orthonormal bases to orthonormal bases, and T^*T=I are equivalent Theorem
- Galois orbits classify simple modules after splitting base change Theorem
- If χ_T(x)=∏_i<n(x-λᵢ) in F[x], then χ_p(T)(y)=∏_i<n(y-p(λᵢ)) for every p∈ F[x]: the eigenvalues of p(T) are p(λᵢ), counted with algebraic multiplicity Theorem
- In a finite-dimensional vector space, a decomposable wedge is nonzero exactly when its vectors are linearly independent Theorem
- Sylvester's law of inertia: every real symmetric form is congruent to diag(Iₚ,-I_q,0ᵣ), and (p,q,r) is unique Theorem
- The best rank-at-most-k approximation in operator norm is the rank-k truncation of a singular value decomposition 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
- The geometric multiplicity of an eigenvalue does not exceed its algebraic multiplicity Theorem
Dependency tree · two levels
57 results within two dependency steps of this one, each drawn at its shortest distance from it. An arrow runs from a result to what uses it, so the chart reads left to right and ends at this result, which carries a heavier outline. Every node is a link to that result. Click elsewhere on 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)
- Purdue University Algebra 511 notes: Dimension (standard reference, not scraped)
- Sheldon Axler, Linear Algebra Done Right, 4th ed. (standard reference, not scraped)