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Linear algebra (Osnabrück 2024-2025)/Part I/Lecture 7

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Characterization of a basis

The following theorem gives an important characterization for a family of vectors to be basis.


Let be a field, and let be a -vector space. Let

be a family of vectors. Then the following statements are equivalent.
  1. The family is a basis of .
  2. 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.
  3. For every vector , there is exactly one representation
  4. The family is maximal linearly independent, that is, as soon as some vector is added, the family is not linearly independent any more.

Proof by ring closure. . The family is a generating system. Let us remove a vector, say , from the family. We have to show that the remaining family, that is , is not a generating system anymore. So suppose that it is still a generating system. Then, in particular, can be written as a linear combination of the remaining vectors, and we have

But then

is a non-trivial representation of , contradicting the linear independence of the family. . Due to the condition, the family is a generating system, hence every vector can be presented as a linear combination. Suppose that for some , there is more than one representation, say

where at least one coefficient is different. Without loss of generality we may assume . Then we get the relation

Because of , we can divide by this number and obtain a representation of using the other vectors. In this situation, due to exercise, also the family without is a generating system of , contradicting the minimality. . Because of the unique representability, the zero vector has only the trivial representation. This means that the vectors are linearly independent. If we add a vector , then it has a representation

and therefore

is a non-trivial representation of , so that the extended family is not linearly independent. . The family is linearly independent, we have to show that it is also a generating system. Let . Due to the condition, the family is not linearly independent. This means that there exists a non-trivial representation

Here , because else this would be a non-trivial representation of with the original family . Hence, we can write

yielding a representation for .



Let a basis of a -vector space be given. Due to fact, this means that for every vector there exists a unique representation (a linear combination)

Here, the uniquely determined elements (scalars) are called the coordinates of with respect to the given basis. This means that for a given basis, there is a correspondence between vectors and coordinate tuples . We say that a basis determines a linear coordinate system[1] of . To paraphrase, a basis gives in particular a bijective mapping

The inverse mapping

is also called the coordinate mapping.


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. 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.





Footnotes
  1. Linear coordinates give a bijective relation between points and number tuples. Due to linearity, such a bijection respects the addition and the 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.


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