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# 59 Vector Spaces

### Sections

1. Constructing Vector Spaces
2. Operations and Attributes for Vector Spaces
3. Domains of Subspaces of Vector Spaces
4. Bases of Vector Spaces
5. Operations for Vector Space Bases
6. Operations for Special Kinds of Bases
7. Mutable Bases
8. Row and Matrix Spaces
9. Vector Space Homomorphisms
10. Vector Spaces Handled By Nice Bases
11. How to Implement New Kinds of Vector Spaces

- `IsLeftVectorSpace(` V`) C`
- `IsVectorSpace(` V`) C`

  A vector space in GAP is a free left module (see IsFreeLeftModule) over a division ring (see Chapter Fields and Division Rings).

  Whenever we talk about an F-vector space V then V is an additive group (see IsAdditiveGroup) on which the division ring F acts via multiplication from the left such that this action and the addition in V are left and right distributive. The division ring F can be accessed as value of the attribute `LeftActingDomain` (see LeftActingDomain).

  The characteristic (see Characteristic) of a vector space is equal to the characteristic of its left acting domain.

  Vector spaces in GAP are always left vector spaces, `IsLeftVectorSpace` and `IsVectorSpace` are synonyms.

  ## 59.1 Constructing Vector Spaces

- `VectorSpace(` F`,` gens`[,` zero`][, "basis"] ) F`

  For a field F and a collection gens of vectors, `VectorSpace` returns the F-vector space spanned by the elements in gens.

  The optional argument zero can be used to specify the zero element of the space; zero must be given if gens is empty. The optional string `"basis"` indicates that gens is known to be linearly independent over F, in particular the dimension of the vector space is immediately set; note that `Basis` (see Basis) need not return the basis formed by gens if the argument `"basis"` is given.

      gap> V:= VectorSpace( Rationals, [ [ 1, 2, 3 ], [ 1, 1, 1 ] ] );
      <vector space over Rationals, with 2 generators>

- `Subspace(` V`,` gens`[, "basis"] ) F`
- `SubspaceNC(` V`,` gens`[, "basis"] ) F`

  For an F-vector space V and a list or collection gens that is a subset of V, `Subspace` returns the F-vector space spanned by gens; if gens is empty then the trivial subspace (see TrivialSubspace) of V is returned. The parent (see Parents) of the returned vector space is set to V.

  `SubspaceNC` does the same as `Subspace`, except that it omits the check whether gens is a subset of V.

  The optional string `"basis"` indicates that gens is known to be linearly independent over F. In this case the dimension of the subspace is immediately set, and both `Subspace` and `SubspaceNC` do not check whether gens really is linearly independent and whether gens is a subset of V.

      gap> V:= VectorSpace( Rationals, [ [ 1, 2, 3 ], [ 1, 1, 1 ] ] );;
      gap> W:= Subspace( V, [ [ 0, 1, 2 ] ] );
      <vector space over Rationals, with 1 generators>

- `AsVectorSpace(` F`,` D`) O`

  Let F be a division ring and D a domain. If the elements in D form an F-vector space then `AsVectorSpace` returns this F-vector space, otherwise `fail` is returned.

  `AsVectorSpace` can be used for example to view a given vector space as a vector space over a smaller or larger division ring.

      gap> V:= FullRowSpace( GF( 27 ), 3 );
      ( GF(3^3)^3 )
      gap> Dimension( V );  LeftActingDomain( V );
      3
      GF(3^3)
      gap> W:= AsVectorSpace( GF( 3 ), V );
      <vector space over GF(3), with 9 generators>
      gap> Dimension( W );  LeftActingDomain( W );
      9
      GF(3)
      gap> AsVectorSpace( GF( 9 ), V );
      fail

- `AsSubspace(` V`,` U`) O`

  Let V be an F-vector space, and U a collection. If U is a subset of V such that the elements of U form an F-vector space then `AsSubspace` returns this vector space, with parent set to V (see AsVectorSpace). Otherwise `fail` is returned.

      gap> V:= VectorSpace( Rationals, [ [ 1, 2, 3 ], [ 1, 1, 1 ] ] );;
      gap> W:= VectorSpace( Rationals, [ [ 1/2, 1/2, 1/2 ] ] );;
      gap> U:= AsSubspace( V, W );
      <vector space over Rationals, with 1 generators>
      gap> Parent( U ) = V;
      true
      gap> AsSubspace( V, [ [ 1, 1, 1 ] ] );
      fail

  ## 59.2 Operations and Attributes for Vector Spaces

- `GeneratorsOfLeftVectorSpace(` V`) A`
- `GeneratorsOfVectorSpace(` V`) A`

  For an F-vector space V, `GeneratorsOfLeftVectorSpace` returns a list of vectors in V that generate V as an F-vector space.

      gap> GeneratorsOfVectorSpace( FullRowSpace( Rationals, 3 ) );
      [ [ 1, 0, 0 ], [ 0, 1, 0 ], [ 0, 0, 1 ] ]

- `TrivialSubspace(` V`) A`

  For a vector space V, `TrivialSubspace` returns the subspace of V that consists of the zero vector in V.

      gap> V:= GF(3)^3;;
      gap> triv:= TrivialSubspace( V );
      <vector space over GF(3), with 0 generators>
      gap> AsSet( triv );
      [ [ 0*Z(3), 0*Z(3), 0*Z(3) ] ]

  ## 59.3 Domains of Subspaces of Vector Spaces

- `Subspaces(` V`) A`
- `Subspaces(` V`,` k`) O`

  Let V be a finite vector space. In the first form, `Subspaces` returns the domain of all subspaces of V. In the second form, k must be a nonnegative integer, and `Subspaces` returns the domain of all k-dimensional subspaces of V.

  Special `Size` and `Iterator` methods are provided for these domains.

- `IsSubspacesVectorSpace(` D`) C`

  The domain of all subspaces of a (finite) vector space or of all subspaces of fixed dimension, as returned by `Subspaces` (see Subspaces) lies in the category `IsSubspacesVectorSpace`.

      gap> D:= Subspaces( GF(3)^3 );
      Subspaces( ( GF(3)^3 ) )
      gap> Size( D );
      28
      gap> iter:= Iterator( D );;
      gap> NextIterator( iter );
      <vector space over GF(3), with 0 generators>
      gap> NextIterator( iter );
      <vector space of dimension 1 over GF(3)>
      gap> IsSubspacesVectorSpace( D );
      true

  ## 59.4 Bases of Vector Spaces

  In GAP, a basis of a free left F-module V is a list of vectors B = [ v1, v2, ¼, vn ] in V such that V is generated as a left F-module by these vectors and such that B is linearly independent over F. The integer n is the dimension of V (see Dimension). In particular, as each basis is a list (see Chapter Lists), it has a length (see Length), and the i-th vector of B can be accessed as B[i].

      gap> V:= Rationals^3;
      ( Rationals^3 )
      gap> B:= Basis( V );
      CanonicalBasis( ( Rationals^3 ) )
      gap> Length( B );
      3
      gap> B[1];
      [ 1, 0, 0 ]

  The operations described below make sense only for bases of finite dimensional vector spaces. (In practice this means that the vector spaces must be low dimensional, that is, the dimension should not exceed a few hundred.)

  Besides the basic operations for lists (see Basic Operations for Lists), the basic operations for bases are `BasisVectors` (see BasisVectors), `Coefficients` (see Coefficients), `LinearCombination` (see LinearCombination), and `UnderlyingLeftModule` (see UnderlyingLeftModule). These and other operations for arbitrary bases are described in Operations for Vector Space Bases.

  For special kinds of bases, further operations are defined (see Operations for Special Kinds of Bases).

  GAP supports the following three kinds of bases.

  Relative bases delegate the work to other bases of the same free left module, via basechange matrices (see RelativeBasis).

  Bases handled by nice bases delegate the work to bases of isomorphic left modules over the same left acting domain (see Vector Spaces Handled By Nice Bases).

  Finally, of course there must be bases in GAP that really do the work.

  For example, in the case of a Gaussian row or matrix space V (see Row and Matrix Spaces), `Basis(` V`)` is a semi-echelonized basis (see IsSemiEchelonized) that uses Gaussian elimination; such a basis is of the third kind. `Basis(` V`,` vectors`)` is either semi-echelonized or a relative basis. Other examples of bases of the third kind are canonical bases of finite fields and of abelian number fields.

  Bases handled by nice bases are described in Vector Spaces Handled By Nice Bases. Examples are non-Gaussian row and matrix spaces, and subspaces of finite fields and abelian number fields that are themselves not fields.

- `IsBasis(` obj`) C`

  In GAP, a basis of a free left module is an object that knows how to compute coefficients w.r.t. its basis vectors (see Coefficients). Bases are constructed by `Basis` (see Basis). Each basis is an immutable list, the i-th entry being the i-th basis vector.

  (See Mutable Bases for mutable bases.)

      gap> V:= GF(2)^2;;
      gap> B:= Basis( V );;
      gap> IsBasis( B );
      true
      gap> IsBasis( [ [ 1, 0 ], [ 0, 1 ] ] );
      false
      gap> IsBasis( Basis( Rationals^2, [ [ 1, 0 ], [ 0, 1 ] ] ) );
      true

- `Basis(` V`) A`
- `Basis(` V`,` vectors`) O`
- `BasisNC(` V`,` vectors`) O`

  Called with a free left F-module V as the only argument, `Basis` returns an F-basis of V whose vectors are not further specified.

  If additionally a list vectors of vectors in V is given that forms an F-basis of V then `Basis` returns this basis; if vectors is not linearly independent over F or does not generate V as a free left F-module then `fail` is returned.

  `BasisNC` does the same as `Basis` for two arguments, except that it does not check whether vectors form a basis.

  If no basis vectors are prescribed then `Basis` need not compute basis vectors; in this case, the vectors are computed in the first call to `BasisVectors`.

      gap> V:= VectorSpace( Rationals, [ [ 1, 2, 7 ], [ 1/2, 1/3, 5 ] ] );;
      gap> B:= Basis( V );
      SemiEchelonBasis( <vector space over Rationals, with 2 generators>, ... )
      gap> BasisVectors( B );
      [ [ 1, 2, 7 ], [ 0, 1, -9/4 ] ]
      gap> B:= Basis( V, [ [ 1, 2, 7 ], [ 3, 2, 30 ] ] );
      Basis( <vector space over Rationals, with 2 generators>,
      [ [ 1, 2, 7 ], [ 3, 2, 30 ] ] )
      gap> Basis( V, [ [ 1, 2, 3 ] ] );
      fail

- `CanonicalBasis(` V`) A`

  If the vector space V supports a canonical basis then `CanonicalBasis` returns this basis, otherwise `fail` is returned.

  The defining property of a canonical basis is that its vectors are uniquely determined by the vector space. If canonical bases exist for two vector spaces over the same left acting domain (see LeftActingDomain) then the equality of these vector spaces can be decided by comparing the canonical bases.

  The exact meaning of a canonical basis depends on the type of V. Canonical bases are defined for example for Gaussian row and matrix spaces (see Row and Matrix Spaces).

  If one designs a new kind of vector spaces (see How to Implement New Kinds of Vector Spaces) and defines a canonical basis for these spaces then the `CanonicalBasis` method one installs (see InstallMethod in ``Programming in GAP'') must not call `Basis`. On the other hand, one probably should install a `Basis` method that simply calls `CanonicalBasis`, the value of the method (see Method Installation and Applicable Methods and Method Selection in ``Programming in GAP'') being `CANONICAL_BASIS_FLAGS`.

      gap> vecs:= [ [ 1, 2, 3 ], [ 1, 1, 1 ], [ 1, 1, 1 ] ];;
      gap> V:= VectorSpace( Rationals, vecs );;
      gap> B:= CanonicalBasis( V );
      CanonicalBasis( <vector space over Rationals, with 3 generators> )
      gap> BasisVectors( B );
      [ [ 1, 0, -1 ], [ 0, 1, 2 ] ]

- `RelativeBasis(` B`,` vectors`) O`
- `RelativeBasisNC(` B`,` vectors`) O`

  A relative basis is a basis of the free left module V that delegates the computation of coefficients etc. to another basis of V via a basechange matrix.

  Let B be a basis of the free left module V, and vectors a list of vectors in V.

  `RelativeBasis` checks whether vectors form a basis of V, and in this case a basis is returned in which vectors are the basis vectors; otherwise `fail` is returned.

  `RelativeBasisNC` does the same, except that it omits the check.

  ## 59.5 Operations for Vector Space Bases

- `BasisVectors(` B`) A`

  For a vector space basis B, `BasisVectors` returns the list of basis vectors of B. The lists B and `BasisVectors(` B`)` are equal; the main purpose of `BasisVectors` is to provide access to a list of vectors that does not know about an underlying vector space.

      gap> V:= VectorSpace( Rationals, [ [ 1, 2, 7 ], [ 1/2, 1/3, 5 ] ] );;
      gap> B:= Basis( V, [ [ 1, 2, 7 ], [ 0, 1, -9/4 ] ] );;
      gap> BasisVectors( B );
      [ [ 1, 2, 7 ], [ 0, 1, -9/4 ] ]

- `UnderlyingLeftModule(` B`) A`

  For a basis B of a free left module V, say, `UnderlyingLeftModule` returns V.

  The reason why a basis stores a free left module is that otherwise one would have to store the basis vectors and the coefficient domain separately. Storing the module allows one for example to deal with bases whose basis vectors have not yet been computed yet (see Basis); furthermore, in some cases it is convenient to test membership of a vector in the module before computing coefficients w.r.t. a basis.

      gap> B:= Basis( GF(2)^6 );;  UnderlyingLeftModule( B );
      ( GF(2)^6 )

- `Coefficients(` B`,` v`) O`

  Let V be the underlying left module of the basis B, and v a vector such that the family of v is the elements family of the family of V. Then `Coefficients(` B`,` v`)` is the list of coefficients of v w.r.t. B if v lies in V, and `fail` otherwise.

      gap> V:= VectorSpace( Rationals, [ [ 1, 2, 7 ], [ 1/2, 1/3, 5 ] ] );;
      gap> B:= Basis( V, [ [ 1, 2, 7 ], [ 0, 1, -9/4 ] ] );;
      gap> Coefficients( B, [ 1/2, 1/3, 5 ] );
      [ 1/2, -2/3 ]
      gap> Coefficients( B, [ 1, 0, 0 ] );
      fail

- `LinearCombination(` B`,` coeff`) O`
- `LinearCombination(` vectors`,` coeff`) O`

  If B is a basis of length n, say, and coeff is a row vector of the same length as B, `LinearCombination` returns the vector åi=1n coeff [i] * B [i].

  If vectors and coeff are homogeneous lists of the same length n, say, `LinearCombination` returns the vector åi=1n coeff [i]*vectors [i]. Perhaps the most important usage is the case where vectors forms a basis.

      gap> V:= VectorSpace( Rationals, [ [ 1, 2, 7 ], [ 1/2, 1/3, 5 ] ] );;
      gap> B:= Basis( V, [ [ 1, 2, 7 ], [ 0, 1, -9/4 ] ] );;
      gap> LinearCombination( B, [ 1/2, -2/3 ] );
      [ 1/2, 1/3, 5 ]

- `EnumeratorByBasis(` B`) A`

  For a basis B of the free left F-module V of dimension n, say, `EnumeratorByBasis` returns an enumerator that loops over the elements of V as linear combinations of the vectors of B with coefficients the row vectors in the full row space (see FullRowSpace) of dimension n over F, in the succession given by the default enumerator of this row space.

      gap> V:= GF(2)^3;;
      gap> enum:= EnumeratorByBasis( CanonicalBasis( V ) );;
      gap> Print( enum{ [ 1 .. 4 ] }, "\n" );
      [ [ 0*Z(2), 0*Z(2), 0*Z(2) ], [ 0*Z(2), 0*Z(2), Z(2)^0 ],
        [ 0*Z(2), Z(2)^0, 0*Z(2) ], [ 0*Z(2), Z(2)^0, Z(2)^0 ] ]
      gap> B:= Basis( V, [ [ 1, 1, 1 ], [ 1, 1, 0 ], [ 1, 0, 0 ] ] * Z(2) );;
      gap> enum:= EnumeratorByBasis( B );;
      gap> Print( enum{ [ 1 .. 4 ] }, "\n" );
      [ [ 0*Z(2), 0*Z(2), 0*Z(2) ], [ Z(2)^0, 0*Z(2), 0*Z(2) ],
        [ Z(2)^0, Z(2)^0, 0*Z(2) ], [ 0*Z(2), Z(2)^0, 0*Z(2) ] ]

- `IteratorByBasis(` B`) O`

  For a basis B of the free left F-module V of dimension n, say, `IteratorByBasis` returns an iterator that loops over the elements of V as linear combinations of the vectors of B with coefficients the row vectors in the full row space (see FullRowSpace) of dimension n over F, in the succession given by the default enumerator of this row space.

      gap> V:= GF(2)^3;;
      gap> iter:= IteratorByBasis( CanonicalBasis( V ) );;
      gap> for i in [ 1 .. 4 ] do Print( NextIterator( iter ), "\n" ); od;
      [ 0*Z(2), 0*Z(2), 0*Z(2) ]
      [ 0*Z(2), 0*Z(2), Z(2)^0 ]
      [ 0*Z(2), Z(2)^0, 0*Z(2) ]
      [ 0*Z(2), Z(2)^0, Z(2)^0 ]
      gap> B:= Basis( V, [ [ 1, 1, 1 ], [ 1, 1, 0 ], [ 1, 0, 0 ] ] * Z(2) );;
      gap> iter:= IteratorByBasis( B );;
      gap> for i in [ 1 .. 4 ] do Print( NextIterator( iter ), "\n" ); od;
      [ 0*Z(2), 0*Z(2), 0*Z(2) ]
      [ Z(2)^0, 0*Z(2), 0*Z(2) ]
      [ Z(2)^0, Z(2)^0, 0*Z(2) ]
      [ 0*Z(2), Z(2)^0, 0*Z(2) ]

  ## 59.6 Operations for Special Kinds of Bases

- `IsCanonicalBasis(` B`) P`

  If the underlying free left module V of the basis B supports a canonical basis (see CanonicalBasis) then `IsCanonicalBasis` returns `true` if B is equal to the canonical basis of V, and `false` otherwise.

- `IsIntegralBasis(` B`) P`

  Let B be an S-basis of a field F, say, for a subfield S of F, and let R and M be the rings of algebraic integers in S and F, respectively. `IsIntegralBasis` returns `true` if B is also an R-basis of M, and `false` otherwise.

- `IsNormalBasis(` B`) P`

  Let B be an S-basis of a field F, say, for a subfield S of F. `IsNormalBasis` returns `true` if B is invariant under the Galois group (see GaloisGroup!of field) of the field extension F / S, and `false` otherwise.

      gap> B:= CanonicalBasis( GaussianRationals );
      CanonicalBasis( GaussianRationals )
      gap> IsIntegralBasis( B );  IsNormalBasis( B );
      true
      false

- `StructureConstantsTable(` B`) A`

  Let B be a basis of a free left module R, say, that is also a ring. In this case `StructureConstantsTable` returns a structure constants table T in sparse representation, as used for structure constants algebras (see Section Algebras of the GAP User's Tutorial).

  If B has length n then T is a list of length n+2. The first n entries of T are lists of length n. T[ n+1 ] is one of 1, -1, or 0; in the case of 1 the table is known to be symmetric, in the case of -1 it is known to be antisymmetric, and 0 occurs in all other cases. T[ n+2 ] is the zero element of the coefficient domain.

  The coefficients w.r.t. B of the product of the i-th and j-th basis vector of B are stored in T[i][j] as a list of length 2; its first entry is the list of positions of nonzero coefficients, the second entry is the list of these coefficients themselves.

  The multiplication in an algebra A with vector space basis B with basis vectors [ v1, ¼, vn ] is determined by the so-called structure matrices Mk = [ mijk ]ij, 1 £ k £ n. The Mk are defined by vi vj = åk mi,j,k vk. Let a = [ a1, ¼, an ] and b = [ b1, ¼, bn ]. Then

  ( | å i | ai vi ) ( | å j | bj vj ) = | å i,j | ai bj ( vi vj ) = | å k | ( | å j | ( | å i | ai mi,j,k ) bj ) vk = | å k | ( a Mk btr ) vk.

  In the following example we temporarily increase the line length limit from its default value 80 to 83 in order to get a nicer output format.

      gap> A:= QuaternionAlgebra( Rationals );;
      gap> SizeScreen([ 83, ]);;
      gap> StructureConstantsTable( Basis( A ) );
      [ [ [ [ 1 ], [ 1 ] ], [ [ 2 ], [ 1 ] ], [ [ 3 ], [ 1 ] ], [ [ 4 ], [ 1 ] ] ],
        [ [ [ 2 ], [ 1 ] ], [ [ 1 ], [ -1 ] ], [ [ 4 ], [ 1 ] ], [ [ 3 ], [ -1 ] ] ],
        [ [ [ 3 ], [ 1 ] ], [ [ 4 ], [ -1 ] ], [ [ 1 ], [ -1 ] ], [ [ 2 ], [ 1 ] ] ],
        [ [ [ 4 ], [ 1 ] ], [ [ 3 ], [ 1 ] ], [ [ 2 ], [ -1 ] ], [ [ 1 ], [ -1 ] ] ],
        0, 0 ]
      gap> SizeScreen([ 80, ]);;

  ## 59.7 Mutable Bases

  It is useful to have a mutable basis of a free module when successively closures with new vectors are formed, since one does not want to create a new module and a corresponding basis for each step.

  Note that the situation here is different from the situation with stabilizer chains, which are (mutable or immutable) records that do not need to know about the groups they describe, whereas each (immutable) basis stores the underlying left module (see UnderlyingLeftModule).

  So immutable bases and mutable bases are different categories of objects. The only thing they have in common is that one can ask both for their basis vectors and for the coefficients of a given vector.

  Since `Immutable` produces an immutable copy of any GAP object, it would in principle be possible to construct a mutable basis that is in fact immutable. In the sequel, we will deal only with mutable bases that are in fact mutable GAP objects, hence these objects are unable to store attribute values.

  Basic operations for immutable bases are `NrBasisVectors` (see NrBasisVectors), `IsContainedInSpan` (see IsContainedInSpan), `CloseMutableBasis` (see CloseMutableBasis), `ImmutableBasis` (see ImmutableBasis), `Coefficients` (see Coefficients), and `BasisVectors` (see BasisVectors). `ShallowCopy` (see ShallowCopy) for a mutable basis returns a mutable plain list containing the current basis vectors.

  Since mutable bases do not admit arbitrary changes of their lists of basis vectors, a mutable basis is not a list. It is, however, a collection, more precisely its family (see Families) equals the family of its collection of basis vectors.

  Mutable bases can be constructed with `MutableBasis`.

  Similar to the situation with bases (cf. Bases of Vector Spaces), GAP supports the following three kinds of mutable bases.

  The generic method of `MutableBasis` returns a mutable basis that simply stores an immutable basis; clearly one wants to avoid this whenever possible with reasonable effort.

  There are mutable bases that store a mutable basis for a nicer module. Note that this is meaningful only if the mechanism of computing nice and ugly vectors (see Vector Spaces Handled By Nice Bases) is invariant under closures of the basis; this is the case for example if the vectors are matrices, Lie objects, or elements of structure constants algebras.

  There are mutable bases that use special information to perform their tasks; examples are mutable bases of Gaussian row and matrix spaces.

- `IsMutableBasis(` MB`) C`

  Every mutable basis lies in the category `IsMutableBasis`.

- `MutableBasis(` R`,` vectors`[,` zero`] ) O`

  `MutableBasis` returns a mutable basis for the R-free module generated by the vectors in the list vectors. The optional argument zero is the zero vector of the module; it must be given if vectors is empty.

  Note that vectors will in general not be the basis vectors of the mutable basis!

      gap> MB:= MutableBasis( Rationals, [ [ 1, 2, 3 ], [ 0, 1, 0 ] ] );
      <mutable basis over Rationals, 2 vectors>

- `NrBasisVectors(` MB`) O`

  For a mutable basis MB, `NrBasisVectors` returns the current number of basis vectors of MB. Note that this operation is not an attribute, as it makes no sense to store the value. `NrBasisVectors` is used mainly as an equivalent of `Dimension` for the underlying left module in the case of immutable bases.

      gap> MB:= MutableBasis( Rationals, [ [ 1, 1], [ 2, 2 ] ] );;
      gap> NrBasisVectors( MB );
      1

- `ImmutableBasis(` MB`[,` V`] ) O`

  `ImmutableBasis` returns the immutable basis B, say, with the same basis vectors as in the mutable basis MB.

  If the second argument V is present then V is the value of `UnderlyingLeftModule` (see UnderlyingLeftModule) for B. The second variant is used mainly for the case that one knows the module for the desired basis in advance, and if it has a nicer structure than the module known to MB, for example if it is an algebra.

      gap> MB:= MutableBasis( Rationals, [ [ 1, 1 ], [ 2, 2 ] ] );;
      gap> B:= ImmutableBasis( MB );
      SemiEchelonBasis( <vector space of dimension 1 over Rationals>, [ [ 1, 1 ] ] )
      gap> UnderlyingLeftModule( B );
      <vector space of dimension 1 over Rationals>

- `IsContainedInSpan(` MB`,` v`) O`

  For a mutable basis MB over the coefficient ring R, say, and a vector v, `IsContainedInSpan` returns `true` is v lies in the R-span of the current basis vectors of MB, and `false` otherwise.

- `CloseMutableBasis(` MB`,` v`) O`

  For a mutable basis MB over the coefficient ring R, say, and a vector v, `CloseMutableBasis` changes MB such that afterwards it describes the R-span of the former basis vectors together with v.

  Note that if v enlarges the dimension then this does in general not mean that v is simply added to the basis vectors of MB. Usually a linear combination of v and the other basis vectors is added, and also the old basis vectors may be modified, for example in order to keep the list of basis vectors echelonized (see IsSemiEchelonized).

      gap> MB:= MutableBasis( Rationals, [ [ 1, 1, 3 ], [ 2, 2, 1 ] ] );
      <mutable basis over Rationals, 2 vectors>
      gap> IsContainedInSpan( MB, [ 1, 0, 0 ] );
      false
      gap> CloseMutableBasis( MB, [ 1, 0, 0 ] );
      gap> MB;
      <mutable basis over Rationals, 3 vectors>
      gap> IsContainedInSpan( MB, [ 1, 0, 0 ] );
      true

  ## 59.8 Row and Matrix Spaces

- `IsRowSpace(` V`) F`

  A row space in GAP is a vector space that consists of row vectors (see Chapter Row Vectors).

- `IsMatrixSpace(` V`) F`

  A matrix space in GAP is a vector space that consists of matrices (see Chapter Matrices).

- `IsGaussianSpace(` V`) F`

  The filter `IsGaussianSpace` (see Filters) for the row space (see IsRowSpace) or matrix space (see IsMatrixSpace) V over the field F, say, indicates that the entries of all row vectors or matrices in V, respectively, are all contained in F. In this case, V is called a Gaussian vector space. Bases for Gaussian spaces can be computed using Gaussian elimination for a given list of vector space generators.

      gap> mats:= [ [[1,1],[2,2]], [[3,4],[0,1]] ];;
      gap> V:= VectorSpace( Rationals, mats );;
      gap> IsGaussianSpace( V );
      true
      gap> mats[1][1][1]:= E(4);;   # an element in an extension field
      gap> V:= VectorSpace( Rationals, mats );;
      gap> IsGaussianSpace( V );
      false
      gap> V:= VectorSpace( Field( Rationals, [ E(4) ] ), mats );;
      gap> IsGaussianSpace( V );
      true

- `FullRowSpace(` F`,` n`) F`

  For a field F and a nonnegative integer n, `FullRowSpace` returns the F-vector space that consists of all row vectors (see IsRowVector) of length n with entries in F.

  An alternative to construct this vector space is via ``F`^`n``.

      gap> FullRowSpace( GF( 9 ), 3 );
      ( GF(3^2)^3 )
      gap> GF(9)^3;           # the same as above
      ( GF(3^2)^3 )

- `FullMatrixSpace(` F`,` m`,` n`) F`

  For a field F and two positive integers m and n, `FullMatrixSpace` returns the F-vector space that consists of all m by n matrices (see IsMatrix) with entries in F.

  If ``m`=` n`` then the result is in fact an algebra (see FullMatrixAlgebra).

  An alternative to construct this vector space is via ``F`^[`m`,`n`]`.

      gap> FullMatrixSpace( GF(2), 4, 5 );
      ( GF(2)^[ 4, 5 ] )
      gap> GF(2)^[ 4, 5 ];    # the same as above
      ( GF(2)^[ 4, 5 ] )

- `DimensionOfVectors(` M`) A`

  For a left module M that consists of row vectors (see IsRowModule), `DimensionOfVectors` returns the common length of all row vectors in M. For a left module M that consists of matrices (see IsMatrixModule), `DimensionOfVectors` returns the common matrix dimensions (see DimensionsMat) of all matrices in M.

      gap> DimensionOfVectors( GF(2)^5 );
      5
      gap> DimensionOfVectors( GF(2)^[2,3] );
      [ 2, 3 ]

- `IsSemiEchelonized(` B`) P`

  Let B be a basis of a Gaussian row or matrix space V, say (see IsGaussianSpace) over the field F.

  If V is a row space then B is semi-echelonized if the matrix formed by its basis vectors has the property that the first nonzero element in each row is the identity of F, and all values exactly below these pivot elements are the zero of F (cf. SemiEchelonMat).

  If V is a matrix space then B is semi-echelonized if the matrix obtained by replacing each basis vector by the concatenation of its rows is semi-echelonized (see above, cf. SemiEchelonMats).

      gap> V:= GF(2)^2;;
      gap> B1:= Basis( V, [ [ 0, 1 ], [ 1, 0 ] ] * Z(2) );;
      gap> IsSemiEchelonized( B1 );
      true
      gap> B2:= Basis( V, [ [ 0, 1 ], [ 1, 1 ] ] * Z(2) );;
      gap> IsSemiEchelonized( B2 );
      false

- `SemiEchelonBasis(` V`) A`
- `SemiEchelonBasis(` V`,` vectors`) O`
- `SemiEchelonBasisNC(` V`,` vectors`) O`

  Let V be a Gaussian row or matrix vector space over the field F (see IsGaussianSpace, IsRowSpace, IsMatrixSpace).

  Called with V as the only argument, `SemiEchelonBasis` returns a basis of V that has the property `IsSemiEchelonized` (see IsSemiEchelonized).

  If additionally a list vectors of vectors in V is given that forms a semi-echelonized basis of V then `SemiEchelonBasis` returns this basis; if vectors do not form a basis of V then `fail` is returned.

  `SemiEchelonBasisNC` does the same as `SemiEchelonBasis` for two arguments, except that it is not checked whether vectors form a semi-echelonized basis.

      gap> V:= GF(2)^2;;
      gap> B:= SemiEchelonBasis( V );
      SemiEchelonBasis( ( GF(2)^2 ), ... )
      gap> Print( BasisVectors( B ), "\n" );
      [ [ Z(2)^0, 0*Z(2) ], [ 0*Z(2), Z(2)^0 ] ]
      gap> B:= SemiEchelonBasis( V, [ [ 1, 1 ], [ 0, 1 ] ] * Z(2) );
      SemiEchelonBasis( ( GF(2)^2 ), <an immutable 2x2 matrix over GF2> )
      gap> Print( BasisVectors( B ), "\n" );
      [ [ Z(2)^0, Z(2)^0 ], [ 0*Z(2), Z(2)^0 ] ]
      gap> Coefficients( B, [ 0, 1 ] * Z(2) );
      [ 0*Z(2), Z(2)^0 ]
      gap> Coefficients( B, [ 1, 0 ] * Z(2) );
      [ Z(2)^0, Z(2)^0 ]
      gap> SemiEchelonBasis( V, [ [ 0, 1 ], [ 1, 1 ] ] * Z(2) );
      fail

- `IsCanonicalBasisFullRowModule(` B`) P`

  `IsCanonicalBasisFullRowModule` returns `true` if B is the canonical basis (see IsCanonicalBasis) of a full row module (see IsFullRowModule), and `false` otherwise.

  The canonical basis of a Gaussian row space is defined as the unique semi-echelonized (see IsSemiEchelonized) basis with the additional property that for j > i the position of the pivot of row j is bigger than the position of the pivot of row i, and that each pivot column contains exactly one nonzero entry.

- `IsCanonicalBasisFullMatrixModule(` B`) P`

  `IsCanonicalBasisFullMatrixModule` returns `true` if B is the canonical basis (see IsCanonicalBasis) of a full matrix module (see IsFullMatrixModule), and `false` otherwise.

  The canonical basis of a Gaussian matrix space is defined as the unique semi-echelonized (see IsSemiEchelonized) basis for which the list of concatenations of the basis vectors forms the canonical basis of the corresponding Gaussian row space.

- `NormedRowVectors(` V`) A`

  For a finite Gaussian row space V (see IsRowSpace, IsGaussianSpace), `NormedRowVectors` returns a list of those nonzero vectors in V that have a one in the first nonzero component.

  The result list can be used as action domain for the action of a matrix group via `OnLines` (see OnLines), which yields the natural action on one-dimensional subspaces of V (see also Subspaces).

      gap> vecs:= NormedRowVectors( GF(3)^2 );
      [ [ 0*Z(3), Z(3)^0 ], [ Z(3)^0, 0*Z(3) ], [ Z(3)^0, Z(3)^0 ],
        [ Z(3)^0, Z(3) ] ]
      gap> Action( GL(2,3), vecs, OnLines );
      Group([ (3,4), (1,2,4) ])

- `SiftedVector(` B`,` v`) O`

  Let B be a semi-echelonized basis (see IsSemiEchelonized) of a Gaussian row or matrix space V (see IsGaussianSpace), and v a row vector or matrix, respectively, of the same dimension as the elements in V. `SiftedVector` returns the residuum of v with respect to B, which is obtained by successively cleaning the pivot positions in v by subtracting multiples of the basis vectors in B. So the result is the zero vector in V if and only if v lies in V.

  B may also be a mutable basis (see Mutable Bases) of a Gaussian row or matrix space.

      gap> V:= VectorSpace( Rationals, [ [ 1, 2, 7 ], [ 1/2, 1/3, 5 ] ] );;
      gap> B:= Basis( V );;
      gap> SiftedVector( B, [ 1, 2, 8 ] );
      [ 0, 0, 1 ]

  ## 59.9 Vector Space Homomorphisms

  Vector space homomorphisms (or linear mappings) are defined in Section Linear Mappings. GAP provides special functions to construct a particular linear mapping from images of given elements in the source, from a matrix of coefficients, or as a natural epimorphism.

  F-linear mappings with same source and same range can be added, so one can form vector spaces of linear mappings.

- `LeftModuleGeneralMappingByImages(` V`,` W`,` gens`,` imgs`) O`

  Let V and W be two left modules over the same left acting domain R, say, and gens and imgs lists (of the same length) of elements in V and W, respectively. `LeftModuleGeneralMappingByImages` returns the general mapping with source V and range W that is defined by mapping the elements in gens to the corresponding elements in imgs, and taking the R-linear closure.

  gens need not generate V as a left R-module, and if the specification does not define a linear mapping then the result will be multi-valued; hence in general it is not a mapping (see IsMapping).

      gap> V:= Rationals^2;;
      gap> W:= VectorSpace( Rationals, [ [1,2,3], [1,0,1] ] );;
      gap> f:= LeftModuleGeneralMappingByImages( V, W,
      >                                [[1,0],[2,0]], [[1,0,1],[1,0,1] ] );
      [ [ 1, 0 ], [ 2, 0 ] ] -> [ [ 1, 0, 1 ], [ 1, 0, 1 ] ]
      gap> IsMapping( f );
      false

- `LeftModuleHomomorphismByImages(` V`,` W`,` gens`,` imgs`) F`
- `LeftModuleHomomorphismByImagesNC(` V`,` W`,` gens`,` imgs`) O`

  Let V and W be two left modules over the same left acting domain R, say, and gens and imgs lists (of the same length) of elements in V and W, respectively. `LeftModuleHomomorphismByImages` returns the left R-module homomorphism with source V and range W that is defined by mapping the elements in gens to the corresponding elements in imgs.

  If gens does not generate V or if the homomorphism does not exist (i.e., if mapping the generators describes only a multi-valued mapping) then `fail` is returned. For creating a possibly multi-valued mapping from V to W that respects addition, multiplication, and scalar multiplication, `LeftModuleGeneralMappingByImages` can be used.

  `LeftModuleHomomorphismByImagesNC` does the same as `LeftModuleHomomorphismByImages`, except that it omits all checks.

      gap> V:=Rationals^2;;
      gap> W:=VectorSpace( Rationals, [ [ 1, 0, 1 ], [ 1, 2, 3 ] ] );;
      gap> f:=LeftModuleHomomorphismByImages( V, W,
      > [ [ 1, 0 ], [ 0, 1 ] ], [ [ 1, 0, 1 ], [ 1, 2, 3 ] ] );
      [ [ 1, 0 ], [ 0, 1 ] ] -> [ [ 1, 0, 1 ], [ 1, 2, 3 ] ]
      gap> Image( f, [1,1] );
      [ 2, 2, 4 ]

- `LeftModuleHomomorphismByMatrix(` BS`,` matrix`,` BR`) O`

  Let BS and BR be bases of the left R-modules V and W, respectively. `LeftModuleHomomorphismByMatrix` returns the R-linear mapping from V to W that is defined by the matrix matrix as follows. The image of the i-th basis vector of BS is the linear combination of the basis vectors of BR with coefficients the i-th row of matrix.

      gap> V:= Rationals^2;;
      gap> W:= VectorSpace( Rationals, [ [ 1, 0, 1 ], [ 1, 2, 3 ] ] );;
      gap> f:= LeftModuleHomomorphismByMatrix( Basis( V ),
      > [ [ 1, 2 ], [ 3, 1 ] ], Basis( W ) );
      <linear mapping by matrix, ( Rationals^
      2 ) -> <vector space over Rationals, with 2 generators>>

- `NaturalHomomorphismBySubspace(` V`,` W`) O`

  For an R-vector space V and a subspace W of V, `NaturalHomomorphismBySubspace` returns the R-linear mapping that is the natural projection of V onto the factor space ``V`/` W``.

      gap> V:= Rationals^3;;
      gap> W:= VectorSpace( Rationals, [ [ 1, 1, 1 ] ] );;
      gap> f:= NaturalHomomorphismBySubspace( V, W );
      <linear mapping by matrix, ( Rationals^3 ) -> ( Rationals^2 )>

- `Hom(` F`,` V`,` W`) O`

  For a field F and two vector spaces V and W that can be regarded as F-modules (see AsLeftModule), `Hom` returns the F-vector space of all F-linear mappings from V to W.

      gap> V:= Rationals^2;;
      gap> W:= VectorSpace( Rationals, [ [ 1, 0, 1 ], [ 1, 2, 3 ] ] );;
      gap> H:= Hom( Rationals, V, W );
      Hom( Rationals, ( Rationals^2 ), <vector space of dimension
      2 over Rationals> )
      gap> Dimension( H );
      4

- `End(` F`,` V`) O`

  For a field F and a vector space V that can be regarded as an F-module (see AsLeftModule), `End` returns the F-algebra of all F-linear mappings from V to V.

      gap> A:= End( Rationals, Rationals^2 );
      End( Rationals, ( Rationals^2 ) )
      gap> Dimension( A );
      4

- `IsFullHomModule(` M`) P`

  A full hom module is a module of all R-linear mappings between two left R-modules. The function `Hom` (see Hom) can be used to construct a full hom module.

      gap> V:= Rationals^2;;
      gap> W:= VectorSpace( Rationals, [ [ 1, 0, 1 ], [ 1, 2, 3 ] ] );;
      gap> H:= Hom( Rationals, V, W );;
      gap> IsFullHomModule( H );
      true

- `IsPseudoCanonicalBasisFullHomModule(` B`) P`

  A basis of a full hom module is called pseudo canonical basis if the matrices of its basis vectors w.r.t. the stored bases of source and range contain exactly one identity entry and otherwise zeros.

  Note that this is not a canonical basis (see CanonicalBasis) because it depends on the stored bases of source and range.

      gap> IsPseudoCanonicalBasisFullHomModule( Basis( H ) );
      true

- `IsLinearMappingsModule(` V`) F`

  If an F-vector space V is in the filter `IsLinearMappingsModule` then this expresses that V consists of linear mappings, and that V is handled via the mechanism of nice bases (see Vector Spaces Handled By Nice Bases) in the following way. Let S and R be the source and the range, respectively, of each mapping in V. Then the `NiceFreeLeftModuleInfo` value of V is a record with the components `basissource` (a basis BS of S) and `basisrange` (a basis BR of R), and the `NiceVector` value of v Î V is defined as the matrix of the F-linear mapping v w.r.t. the bases BS and BR.

  ## 59.10 Vector Spaces Handled By Nice Bases

  There are kinds of free R-modules for which efficient computations are possible because the elements are ``nice'', for example subspaces of full row modules or of full matrix modules. In other cases, a ``nice'' canonical basis is known that allows one to do the necessary computations in the corresponding row module, for example algebras given by structure constants.

  In many other situations, one knows at least an isomorphism from the given module V to a ``nicer'' free left module W, in the sense that for each vector in V, the image in W can easily be computed, and analogously for each vector in W, one can compute the preimage in V.

  This allows one to delegate computations w.r.t. a basis B, say, of V to the corresponding basis C, say, of W. We call W the nice free left module of V, and C the nice basis of B. (Note that it may happen that also C delegates questions to a ``nicer'' basis.) The basis B indicates the intended behaviour by the filter `IsBasisByNiceBasis` (see IsBasisByNiceBasis), and stores C as value of the attribute `NiceBasis` (see NiceBasis). V indicates the intended behaviour by the filter `IsHandledByNiceBasis` (see IsHandledByNiceBasis!for vector spaces), and stores W as value of the attribute `NiceFreeLeftModule` (see NiceFreeLeftModule).

  The bijection between V and W is implemented by the functions `NiceVector` (see NiceVector) and `UglyVector` (see UglyVector); additional data needed to compute images and preimages can be stored as value of `NiceFreeLeftModuleInfo` (see NiceFreeLeftModuleInfo).

- `NiceFreeLeftModule(` V`) A`

  For a free left module V that is handled via the mechanism of nice bases, this attribute stores the associated free left module to which the tasks are delegated.

- `NiceVector(` V`,` v`) O`
- `UglyVector(` V`,` r`) O`

  `NiceVector` and `UglyVector` provide the linear bijection between the free left module V and ``W`:= NiceFreeLeftModule(` V`)`.

  If v lies in the elements family of the family of V then `NiceVector(` v`)` is either `fail` or an element in the elements family of the family of W.

  If r lies in the elements family of the family of W then `UglyVector(` r`)` is either `fail` or an element in the elements family of the family of V.

  If v lies in V (which usually cannot be checked without using W) then `UglyVector(` V`, NiceVector(` V`,` v`) ) =` v``. If r lies in W (which usually can be checked) then `NiceVector(` V`, UglyVector(` V`,` r`) ) =` r``.

  (This allows one to implement for example a membership test for V using the membership test in W.)

- `NiceFreeLeftModuleInfo(` V`) A`

  For a free left module V that is handled via the mechanism of nice bases, this operation has to provide the necessary information (if any) for calls of `NiceVector` and `UglyVector` (see NiceVector).

- `NiceBasis(` B`) A`

  Let B be a basis of a free left module V that is handled via nice bases. If B has no basis vectors stored at the time of the first call to `NiceBasis` then `NiceBasis(` B`)` is obtained as `Basis( NiceFreeLeftModule(` V`) )`. If basis vectors are stored then `NiceBasis(` B`)` is the result of the call of `Basis` with arguments `NiceFreeLeftModule(` V`)` and the `NiceVector` values of the basis vectors of B.

  Note that the result is `fail` if and only if the ``basis vectors'' stored in B are in fact not basis vectors.

  The attributes `GeneratorsOfLeftModule` of the underlying left modules of B and the result of `NiceBasis` correspond via `NiceVector` and `UglyVector`.

- `IsBasisByNiceBasis(` B`) C`

  This filter indicates that the basis B delegates tasks such as the computation of coefficients (see Coefficients) to a basis of an isomorphisc ``nicer'' free left module.

- `IsHandledByNiceBasis(` M`) C`

  For a free left module M in this category, essentially all operations are performed using a ``nicer'' free left module, which is usually a row module.

  ## 59.11 How to Implement New Kinds of Vector Spaces

- `DeclareHandlingByNiceBasis(` name`,` info`) F`
- `InstallHandlingByNiceBasis(` name`,` record`) F`

  These functions are used to implement a new kind of free left modules that shall be handled via the mechanism of nice bases (see Vector Spaces Handled By Nice Bases).

  name must be a string, a filter f with this name is created, and a logical implication from f to `IsHandledByNiceBasis` (see IsHandledByNiceBasis!for vector spaces) is installed.

  record must be a record with the following components.

  `detect`
    a function of four arguments R, l, V, and z, where V is a free left module over the ring R with generators the list or collection l, and z is either the zero element of V or `false` (then l is nonempty); the function returns `true` if V shall lie in the filter f, and `false` otherwise; the return value may also be `fail`, which indicates that V is not to be handled via the mechanism of nice bases at all,

  `NiceFreeLeftModuleInfo`
    the `NiceFreeLeftModuleInfo` method for left modules in f,

  `NiceVector`
    the `NiceVector` method for left modules V in f; called with V and a vector v Î V, this function returns the nice vector r associated with v, and

  `UglyVector`
    the `UglyVector` method for left modules V in f; called with V and a vector r in the `NiceFreeLeftModule` value of V, this function returns the vector v Î V to which r is associated.

  The idea is that all one has to do for implementing a new kind of free left modules handled by the mechanism of nice bases is to call `DeclareHandlingByNiceBasis` and `InstallHandlingByNiceBasis`, which causes the installation of the necessary methods and adds the pair [ f, record `.``detect` ] to the global list `NiceBasisFiltersInfo`. The `LeftModuleByGenerators` methods call `CheckForHandlingByNiceBasis` (see CheckForHandlingByNiceBasis), which sets the appropriate filter for the desired left module if applicable.

- `NiceBasisFiltersInfo V`

  An overview of all kinds of vector spaces that are currently handled by nice bases is given by the global list `NiceBasisFiltersInfo`. Examples of such vector spaces are vector spaces of field elements (but not the fields themselves) and non-Gaussian row and matrix spaces (see IsGaussianSpace).

- `CheckForHandlingByNiceBasis(` R`,` gens`,` M`,` zero`) F`

  Whenever a free left module is constructed for which the filter `IsHandledByNiceBasis` may be useful, `CheckForHandlingByNiceBasis` should be called. (This is done in the methods for `VectorSpaceByGenerators`, `AlgebraByGenerators`, `IdealByGenerators` etc. in the GAP library.)

  The arguments of this function are the coefficient ring R, the list gens of generators, the constructed module M itself, and the zero element zero of M; if gens is nonempty then the zero value may also be `false`.

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  GAP 4 manual
  May 2002