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Dimension theorem

Statement

Any two finite bases of the same vector space VV have the same number of elements: if B1B_1 has mm vectors and B2B_2 has kk vectors, then dim⁡(V)=m=k\dim(V)=m=k. Hence dim⁡(V)\dim(V) is a well-defined invariant of VV, not an artifact of which basis was chosen.

Why is it true?

A basis is simultaneously a spanning set and an independent set, so applying the exchange lemma once in each direction — once treating B1B_1 as the spanning set and B2B_2 as independent, once the other way around — squeezes the two sizes between each other until they must be equal.

Proof sketch

Let B1={v1,…,vm}B_1=\{v_1,\dots,v_m\} and B2={w1,…,wk}B_2=\{w_1,\dots,w_k\} both be bases of VV. Since B1B_1 spans VV (it is a basis) and B2B_2 is linearly independent (it is a basis), the exchange lemma proved above applies directly with B1B_1 playing the role of the spanning set and B2B_2 the independent set, giving k≤mk\leq m.

Symmetrically, since B2B_2 spans VV and B1B_1 is linearly independent, apply the exchange lemma again with the roles swapped — B2B_2 as the spanning set, B1B_1 as the independent set — giving m≤km\leq k.

Combining the two inequalities k≤mk\leq m and m≤km\leq k forces k=mk=m: the two bases have exactly the same number of vectors.

Since this argument places no restriction on which particular finite bases B1,B2B_1,B_2 were chosen, every finite basis of VV has this same common size. This justifies defining dim⁡(V)\dim(V) to be that common size — it is an invariant of the space VV itself, never depending on which basis was used to compute it.

Topics that use this theorem

Step-by-step proofs

No step-by-step proof yet for this theorem.

References

  1. Sheldon Axler (2015). Linear Algebra Done Right
  2. Eric W. Weisstein (MathWorld) (2024). Vector Space