# Linear span

In the mathematical subfield of linear algebra or more generally functional analysis, the **linear span** (also called the **linear hull**) of a set of vectors in a vector space is the intersection of all subspaces containing that set. The linear span of a set of vectors is therefore a vector space.

## Contents

## Definition

Given a vector space *V* over a field *K*, the span of a set *S* of vectors (not necessarily finite) is defined to be the intersection *W* of all subspaces of *V* that contain *S*. *W* is referred to as the subspace *spanned by* *S*, or by the vectors in *S*. Conversely, *S* is called a *spanning set* of *W*, and we say that *S* *spans* *W*.

Alternatively, the span of *S* may be defined as the set of all finite linear combinations of elements of *S*, which follows from the above definition.

In particular, if *S* is a finite subset of *V*, then the span of *S* is the set of all linear combinations of the elements of *S*. In the case of infinite *S*, infinite linear combinations (i.e. where a combination may involve an infinite sum, assuming such sums are defined somehow, e.g. if *V* is a Banach space) are excluded by the definition; a generalization that allows these is not equivalent.

## Examples

The real vector space **R**^{3} has {(2,0,0), (0,1,0), (0,0,1)} as a spanning set. This particular spanning set is also a basis. If (2,0,0) were replaced by (1,0,0), it would also form the canonical basis of **R**^{3}.

Another spanning set for the same space is given by {(1,2,3), (0,1,2), (−1,1/2,3), (1,1,1)}, but this set is not a basis, because it is linearly dependent.

The set {(1,0,0), (0,1,0), (1,1,0)} is not a spanning set of **R**^{3}; instead its span is the space of all vectors in **R**^{3} whose last component is zero.

## Theorems

**Theorem 1:** The subspace spanned by a non-empty subset *S* of a vector space *V* is the set of all linear combinations of vectors in *S*.

This theorem is so well known that at times it is referred to as the definition of span of a set.

**Theorem 2:** Every spanning set *S* of a vector space *V* must contain at least as many elements as any linearly independent set of vectors from *V*.

**Theorem 3:** Let *V* be a finite-dimensional vector space. Any set of vectors that spans *V* can be reduced to a basis for *V* by discarding vectors if necessary (i.e. if there are linearly dependent vectors in the set). If the axiom of choice holds, this is true without the assumption that *V* has finite dimension.

This also indicates that a basis is a minimal spanning set when *V* is finite-dimensional.

## Closed linear span

In functional analysis, a closed linear span of a set of vectors is the minimal closed set which contains the linear span of that set.
Suppose that *X* is a normed vector space and let *E* be any non-empty subset of *X*. The **closed linear span** of *E*, denoted by or , is the intersection of all the closed linear subspaces of *X* which contain *E*.

One mathematical formulation of this is

## Notes

The linear span of a set is dense in the closed linear span. Moreover, as stated in the lemma below, the closed linear span is indeed the closure of the linear span.

Closed linear spans are important when dealing with closed linear subspaces (which are themselves highly important, consider Riesz's lemma).

## A useful lemma

Let *X* be a normed space and let *E* be any non-empty subset of *X*. Then

(a) is a closed linear subspace of *X* which contains *E*,

(b) , viz. is the closure of ,

(So the usual way to find the closed linear span is to find the linear span first, and then the closure of that linear span.)

## Matroids

Generalizing the definition of the span of points in space, a subset *X* of the ground set of a matroid is called a *spanning set* if the rank of *X* equals the rank of the entire ground set.

## See also

## External links

- Linear Combinations and Span: Understanding linear combinations and spans of vectors, khanacademy.org.

## References

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- Rynne & Youngson (2001).
*Linear functional analysis*, Springer.

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