Vector space/Basis/Introduction/Section
Let be a field, and let be a -vector space. Then a linearly independent generating system , ,
of is called a basis of .The standard vectors in form a basis. The linear independence was shown in example. To show that they also form a generating system, let
be an arbitrary vector. Then we have immediately
Hence, we have a basis, which is called the standard basis of .
Let be a field, and let be a -vector space. Let
be a family of vectors. Then the following statements are equivalent.- The family is a basis of .
- The family is a minimal generating system; that is, as soon as we remove one vector , the remaining family is not a generating system any more.
- For every vector
,
there is exactly one representation
- The family is maximally linearly independent; that is, as soon as some vector is added, the family is not linearly independent any more.
Proof
Let a basis of a -vector space be given. Due to fact (3), this means that for every vector , there exists a uniquely determined representation
The elements (scalars) are called the coordinates of with respect to the given basis. Thus, for a fixed basis, we have a (bijective) correspondence between the vectors from , and the coordinate tuples . We express this by saying that a basis determines a linear coordinate system.[1]
Let be a field, and let be a -vector space with a finite generating system. Then has a finite
basis.Let , , be a finite generating system of with a finite index set . We argue with the characterization from fact (2). If the family is minimal, then we have a basis. If not, then there exists some such that the remaining family, where is removed, that is, , , is also a generating system. In this case, we can go on with this smaller index set. With this method, we arrive at a subset such that , , is a minimal generating set, hence a basis.
- ↑ Linear coordinates give a bijective relation between points and number tuples. Due to linearity, such a bijection respects addition and scalar multiplication. In many different contexts, also nonlinear (curvilinear) coordinates are important. These put points of a space and number tuples into a bijective relation. Examples are polar coordinates, cylindrical coordinates, and spherical coordinates. By choosing suitable coordinates, mathematical problems, like the computation of volumes, can be simplified.