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Examples. Several of the spaces you've learned about have what are called standard bases:
The standard basis for two-dimensional Euclidean space is {i, j}. |
The standard basis for three-dimensional Euclidean space is {i, j, k}. |
The standard basis for Rn is the set of elementary vectors {e1, e2, ..., en}. |
The standard basis for the space Pn is the set of polynomials {1, x, x2, ..., xn}. |
The standard basis for the space Mnm is the set of m x n matrices with one entry 1 and all the others 0. |
Each of these spaces has many more bases - in Euclidean three-space, for example, any set of three vectors not all in a plane forms a basis.
A set of vectors, linearly independent or not, which spans a space can represent any vector in the space, possibly in several different ways. Once we have a linearly independent spanning set, however, there is only one way to represent each vector.
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Proof. Suppose you had what you thought were two ways of representing the vector v as a linear combination of b1, b2, ..., bn:
Subtract to get
But this is a linearly dependence relation for the basis vectors. Since basis vectors are linearly independent, all the coefficients of this relation must be 0, so c1 = d1, c2 = d2, ..., cn = dn. So in fact, the two ways of representing the vector v turned out to be the same. |
This means we can talk about the way of representing a vector in terms of a basis instead of a way.
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