User:IssaRice/Linear algebra/Properties of a list of vectors and their images

From Machinelearning

Let V and W be two finite-dimensional vector spaces, let T:VW be a linear transformation, and let v1,,vnV be a list of vectors (n is not necessarily equal to dimV or dimW).

Given the above background setting, we can now place various conditions on the list v1,,vn and the transformation T to conclude various things about the list Tv1,,Tvn. For instance, the first row below says that if v1,,vn is a linearly independent list and T is an injective map, then Tv1,,Tvn is also linearly independent.

Condition on v1,,vn Condition on T Conclusion about Tv1,,Tvn Notes
Linearly independent Injective Linearly independent
Spans V Surjective Spans W
Basis for V Bijective Basis for W Combine previous two rows
Basis for V No condition No conclusion Even though we have placed a strong condition on v1,,vn, if we don't place any conditions on T, then there is not much we can conclude about Tv1,,Tvn. If T sends everything to zero, then we just get 0,,0, which neither spans W nor is it a linearly independent list.
No condition Bijective No conclusion Even though we have placed a strong condition on T, if we don't place conditions on the list of vectors, then there is not much we can say about the list of the images. For instance, if V=W=R2, if T is the identity map, and if our list is (1,0),(1,0), then the image is also (1,0),(1,0), which is linearly dependent and does not span W.
Orthonormal Is an isometry Orthonormal
Eigenvectors of T corresponding to distinct non-zero eigenvalues No condition Linearly independent; eigenvectors of T corresponding to distinct eigenvalues Eigenvectors corresponding to distinct eigenvalues are linearly independent, so v1,,vn is a linearly independent list. Since each vj is an eigenvector, we see that Tv1,,Tvn is the list λ1v1,,λnvn, where each λj is the corresponding eigenvalue. Since by assumption none of the eigenvalues are zero, the new list is also linearly independent.