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From Wikipedia, the free encyclopedia

In mathematics, the linear span (also called the linear hull[1] or just span) of a set S of vectors (from a vector space), denoted span(S),[2] is the smallest linear subspace that contains the set.[3] It can be characterized either as the intersection of all linear subspaces that contain S, or as the set of linear combinations of elements of S. The linear span of a set of vectors is therefore a vector space. Spans can be generalized to matroids and modules.

For expressing that a vector space V is a span of a set S, one commonly uses the following phrases: S spans V; S generates V; V is spanned by S; V is generated by S; S is a spanning set of V; S is a generating set of V.

Definition

Given a vector space V over a field K, the span of a set S of vectors (not necessarily infinite) 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 (vectors) of S, which follows from the above definition.[4][5][6][7]

In the case of infinite S, infinite linear combinations (i.e. where a combination may involve an infinite sum, assuming that such sums are defined somehow as in, say, a Banach space) are excluded by the definition; a generalization that allows these is not equivalent.

Examples

The cross-hatched plane is the linear span of u and v in R3.
The cross-hatched plane is the linear span of u and v in R3.

The real vector space R3 has {(−1, 0, 0), (0, 1, 0), (0, 0, 1)} as a spanning set. This particular spanning set is also a basis. If (−1, 0, 0) were replaced by (1, 0, 0), it would also form the canonical basis of R3.

Another spanning set for the same space is given by {(1, 2, 3), (0, 1, 2), (−1, 12, 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 R3, since its span is the space of all vectors in R3 whose last component is zero. That space is also spanned by the set {(1, 0, 0), (0, 1, 0)}, as (1, 1, 0) is a linear combination of (1, 0, 0) and (0, 1, 0). It does, however, span R2.(when interpreted as a subset of R3).

The empty set is a spanning set of {(0, 0, 0)}, since the empty set is a subset of all possible vector spaces in R3, and {(0, 0, 0)} is the intersection of all of these vector spaces.

The set of functions xn where n is a non-negative integer spans the space of polynomials.

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.

Generalizations

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[citation needed].

The vector space definition can also be generalized to modules.[8][9] Given an R-module A and a collection of elements a1, …, an of A, the submodule of A spanned by a1, …, an is the sum of cyclic modules

consisting of all R-linear combinations of the elements ai. As with the case of vector spaces, the submodule of A spanned by any subset of A is the intersection of all submodules containing that subset.

Closed linear span (functional analysis)

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

The closed linear span of the set of functions xn on the interval [0, 1], where n is a non-negative integer, depends on the norm used. If the L2 norm is used, then the closed linear span is the Hilbert space of square-integrable functions on the interval. But if the maximum norm is used, the closed linear span will be the space of continuous functions on the interval. In either case, the closed linear span contains functions that are not polynomials, and so are not in the linear span itself. However, the cardinality of the set of functions in the closed linear span is the cardinality of the continuum, which is the same cardinality as for the set of polynomials.

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, see Riesz's lemma).

A useful lemma

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

  1. is a closed linear subspace of X which contains E,
  2. , 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.)

See also

Citations

  1. ^ Encyclopedia of Mathematics (2020). Linear Hull.
  2. ^ Axler (2015) pp. 29-30, §§ 2.5, 2.8
  3. ^ Axler (2015) p. 29, § 2.7
  4. ^ Hefferon (2020) p. 100, ch. 2, Definition 2.13
  5. ^ Axler (2015) pp. 29-30, §§ 2.5, 2.8
  6. ^ Roman (2005) pp. 41-42
  7. ^ MathWorld (2021) Vector Space Span.
  8. ^ Roman (2005) p. 96, ch. 4
  9. ^ Lane & Birkhoff (1999) p. 193, ch. 6

Sources

Textbook

  • Axler, Sheldon Jay (2015). Linear Algebra Done Right (3rd ed.).  Springer. ISBN 978-3-319-11079-0.
  • Hefferon, Jim (2020). Linear Algebra (4th ed.). Orthogonal Publishing. ISBN 978-1-944325-11-4.
  • Lane, Saunders Mac; Birkhoff, Garrett (1999) [1988]. Algebra (3rd ed.). AMS Chelsea Publishing. ISBN 978-0821816462.
  • Roman, Steven (2005). Advanced Linear Algebra (2nd ed.). Springer. ISBN 0-387-24766-1.
  • Rynne, Brian P.; Youngson, Martin A. (2008). Linear Functional Analysis. Springer. ISBN 978-1848000049.

Web

External links

This page was last edited on 22 October 2021, at 17:18
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