This chapter describes functions which may be used to construct and investigate the structure of matrix groups defined over finite fields.

- Aim of the matrix package
- Contents of the matrix package
- The Developers of the matrix package
- Basic conventions employed in matrix package
- Organisation of this manual
- GModule
- IsGModule
- IsIrreducible for GModules
- IsAbsolutelyIrreducible
- IsSemiLinear
- IsPrimitive for GModules
- IsTensor
- SmashGModule
- HomGModule
- IsomorphismGModule
- CompositionFactors
- Examples
- ClassicalForms
- RecogniseClassical
- ConstructivelyRecogniseClassical
- RecogniseMatrixGroup
- RecogniseClassicalCLG
- RecogniseClassicalNP
- InducedAction
- FieldGenCentMat
- MinimalSubGModules
- SpinBasis
- SemiLinearDecomposition
- TensorProductDecomposition
- SymTensorProductDecomposition
- ExtraSpecialDecomposition
- MinBlocks
- BlockSystemFlag
- Components of a
*G*-module record - ApproximateKernel
- RandomRelations
- DisplayMatRecord
- The record returned by RecogniseMatrixGroup
- DualGModule
- InducedGModule
- PermGModule
- TensorProductGModule
- ImprimitiveWreathProduct
- WreathPower
- PermGroupRepresentation
- GeneralOrthogonalGroup
- OrderMat -- enhanced
- PseudoRandom
- InitPseudoRandom
- IsPpdElement
- SpinorNorm
- Other utility functions
- References

The aim of the matrix package is to provide integrated and comprehensive access to a collection of algorithms, developed primarily over the past decade, for investigating the structure of matrix groups defined over finite fields. We sought to design a package which provides easy access to existing algorithms and implementations, and which both allows new algorithms to be developed easily using existing components, and to update existing ones readily.

Some of the facilities provided are necessarily limited, both on
theoretical and practical grounds; others are **experimental** and
developmental in nature; we welcome criticism of their performance.
One motivation for its release is to encourage input from others.

We summarise the contents of the package and provide references for the relevant algorithms.

(a) Irreducibility and absolutely irreducibility for *G*-modules;
isomorphism testing for irreducible *G*-modules; see Holt and Rees
[5]. The corresponding functions are described in IsIrreducible
for GModules, IsAbsolutelyIrreducible, HomGModule,
IsomorphismGModule, CompositionFactors, FieldGenCentMat,
MinimalSubGModules.

(b) Decide whether a matrix group has certain decompositions with respect to a normal subgroup; see Holt, Leedham-Green, O'Brien and Rees [6]. The corresponding functions are described in IsSemiLinear, SmashGModule, SemiLinearDecomposition, TensorProductDecomposition, SymTensorProductDecomposition, and ExtraSpecialDecomposition.

(c) Decide whether a matrix group is primitive; see Holt, Leedham-Green, O'Brien and Rees [7]. The corresponding functions are described in IsPrimitive for GModules, MinBlocks.

(d) Decide whether a given group contains a classical group in its natural representation. Here we provide access to the algorithms of Celler and Leedham-Green [3] and those of Niemeyer and Praeger [11, 12]. The corresponding function is described in RecogniseClassical, the associated lower-level functions in RecogniseClassicalCLG and RecogniseClassicalNP.

(e) A constructive recognition process for the special linear group developed by Celler and Leedham-Green [4] and described in ConstructivelyRecogniseClassical.

(e) Random element selection; see Celler, Leedham-Green, Murray, Niemeyer and O'Brien [1]. The corresponding functions are described in PseudoRandom, InitPseudoRandom.

(f) Matrix order calculation; see Celler and Leedham-Green [2]. The corresponding functions are described in OrderMat -- enhanced.

(g) Base point selection for the Random Schreier-Sims algorithm for matrix groups; see Murray and O'Brien [10]. The corresponding function is described in PermGroupRepresentation.

(h) Decide whether a matrix group preserves a tensor decomposition; see Leedham-Green and O'Brien [8, 9]. The corresponding function is described in IsTensor.

(i) Recursive exploration of reducible groups; see Pye [13]. The corresponding function is described in RecogniseMatrixGroup.

The algorithms make extensive use of Aschbacher's classification of the maximal subgroups of the general linear group. Possible classes of subgroups mentioned below refer to this classification; see [14, 15] for further details.

In order to access the functions, you must use the command
`RequirePackage`

to load them.

` gap> RequirePackage("matrix");`

The development and organisation of this package was carried out in Aachen by Frank Celler, Eamonn O'Brien and Anthony Pye.

In addition to the new material, this package combines, updates, and replaces material from various contributing sources. These include:

1. Classic package -- originally developed by Celler;

2. Smash package -- originally developed by Holt, Leedham-Green, O'Brien, and Rees;

3. Niemeyer/Praeger classical recognition algorithm -- originally developed by Niemeyer;

4. Recursive code -- originally developed by Pye.

As part of the preparation of this package, much of the contributed code was revised (sometimes significantly) and streamlined, in cooperation with the original developers.

Comments and criticisms are welcome and should be directed to:

Eamonn O'Brien

obrien@math.auckland.ac.nz

A *G*-module is defined by the action of a group *G*, generated by a set
of matrices, on a *d*-dimensional vector space over a field, *F = GF(q)*.

The function `GModule`

returns a *G*-module record, where the component
`.field`

is set to *F*, `.dimension`

to *d*, `.generators`

to the set of
generating matrices for *G*, and `.isGModule`

to true. These components
are set for every *G*-module record constructed using `GModule`

.

Many of the functions described below return or update a *G*-module
record. Additional components which describe the nature of the action of
the underlying group *G* on the *G*-module are set by these functions.
Some of these carry information which may be of general use. These
components are described briefly in "Components of a *G*-module record".
They need not appear in a *G*-module record, or may have the value
`"unknown"`

.

A component `.component`

of a *G*-module record is accessed by
`ComponentFlag`

and its value is set by `SetComponentFlag`

, where the
first letter of the component is capitalised in the function names. For
example, the component `.tensorBasis`

of `module` is set by
`SetTensorBasisFlag( `

and its value accessed by
`module`, `boolean` )`TensorBasisFlag( `

. Such access functions and conventions
also apply to other records constructed by all of these functions.
`module` )

If a function listed below takes as input a matrix group *G*, it also
**usually** accepts a *G*-module.

Sections GModule and IsGModule describe how to construct a *G*-module
from a set of matrices or a group and how to test for a *G*-module.

Sections IsIrreducible for GModules, IsAbsolutelyIrreducible, IsSemiLinear, IsPrimitive for GModules, and IsTensor describe high-level functions which provide access to some of the algorithms mentioned in Contents of the matrix package; these are tests for reducibility, semi-linearity, primitivity, and tensor decomposition, respectively.

Section SmashGModule describes `SmashGModule`

which can be used to
explore whether a matrix group preserves certain decompositions with
respect to a normal subgroup.

Sections HomGModule, IsomorphismGModule, and CompositionFactors
consider homomorphisms between and composition factors of *G*-modules.

Sections ClassicalForms, RecogniseClassical, and ConstructivelyRecogniseClassical describe functions for exploring classical groups.

Section RecogniseMatrixGroup describes the **experimental** function
`RecogniseMatrixGroup`

.

Sections RecogniseClassicalCLG and RecogniseClassicalNP describe the low-level classical recognition functions.

Sections InducedAction, FieldGenCentMat, MinimalSubGModules, and SpinBasis describe the low-level Meataxe functions.

Sections SemiLinearDecomposition, TensorProductDecomposition,
SymTensorProductDecomposition, ExtraSpecialDecomposition,
MinBlocks, BlockSystemFlag, and "Components of a *G*-module record"
describe the low-level `SmashGModule`

functions.

Sections ApproximateKernel, RandomRelations, DisplayMatRecord, and
The record returned by RecogniseMatrixGroup describe the low-level
functions for the function `Re\-co\-gnise\-Matrix\-Group`

.

Sections DualGModule, InducedGModule, PermGModule,
TensorProductGModule, ImprimitiveWreathProduct, and WreathPower
describe functions to construct new *G*-modules from given ones.

Sections PermGroupRepresentation to Other utility functions describe
functions which are somewhat independent of *G*-modules; these include
functions to compute the order of a matrix, construct a permutation
representation for a matrix group, and construct pseudo-random elements
of a group.

Section References provides a bibliography.

`GModule( `

`matrices`, [`F`] )

`GModule( `

`G`, [`F`] )

`GModule`

constructs a *G*-module record from a list `matrices` of
matrices or from a matrix group `G`. The underlying field `F` may be
specified as an optional argument; otherwise, it is taken to be the field
generated by the entries of the given matrices.

The *G*-module record returned contains components `.field`

,
`.dimension`

, `.generators`

and `.isGModule`

.

In using many of the functions described in this chapter, other
components of the *G*-module record may be set, which describe the nature
of the action of the group on the module. A description of these
components is given in "Components of a *G*-module record".

`IsGModule( `

`module` )

If `module` is a record with component `.isGModule`

set to `true`

,
`IsGModule`

returns `true`

, otherwise `false`

.

`IsIrreducible( `

`module` )

`module` is a *G*-module. `IsIrreducible`

tests `module` for
irreducibility, and returns `true`

or `false`

. If `module` is reducible,
a sub- and quotient-module can be constructed using `InducedAction`

(see
InducedAction).

The algorithm is described in [5].

`IsAbsolutelyIrreducible( `

`module` )

The *G*-module `module` is tested for absolute irreducibility, and `true`

or `false`

is returned. If the result is `false`

, then the dimension *e*
of the centralising field of `module` can be accessed by
`DegreeFieldExtFlag(`

. A matrix which centralises `module`)`module`
(that is, it centralises the generating matrices
`GeneratorsFlag(`

) and which has minimal polynomial of degree
`module`)*e* over the ground field can be accessed as `CentMatFlag(`

. If
such a matrix is required with multiplicative order `module`)*q ^{e}-1*, where

`FieldGenCentMat`

(see
FieldGenCentMat) can be called.
The algorithm is described in [5].

`IsSemiLinear( `

`G` )

`IsSemiLinear`

takes as input a matrix group *G* over a finite field and
seeks to decide whether or not *G* acts semilinearly.

The function returns a list containing two values: a boolean and a
*G*-module record, `module`, for *G*. If the boolean is `true`

, then `G`
is semilinear and information about the decomposition can be obtained
using `SemiLinearPartFlag (`

, `module`)`LinearPartFlag (`

, and
`module`)`FrobeniusAutomorphismsFlag (`

. See "Components of a
`module`)*G*-module record" for an explanation of these.

If `IsSemiLinear`

discovers that `G` acts imprimitively, it cannot decide
whether or not `G` acts semilinearly and returns `"unknown"`

.

`SmashGModule`

is called by `IsSemiLinear`

.

The algorithm is described in [6].

`IsPrimitive( `

`G` [, `factorisations`] )

`IsPrimitive`

takes as input a matrix group `G` over a finite field and
seeks to decide whether or not `G` acts primitively. The function
returns a list containing two values: a boolean and a *G*-module
record, `module`, for *G*. If the boolean is `false`

, then `G` is
imprimitive and `BlockSystemFlag (`

returns a block system
(described in MinBlocks).
`module`)

If `IsPrimitive`

discovers that `G` acts semilinearly, then it cannot
decide whether or not `G` acts primitively and returns `"unknown"`

.

The second optional argument is a list of possible factorisations of *d*,
the dimension of *G*. For each *[r, s]* in this list where *rs = d*, the
function seeks to decide whether `G` preserves a non-trivial system of
imprimitivity having *r* blocks of size *s*.

`SmashGModule`

is called by `IsPrimitive`

.

The algorithm is described in [7].

`IsTensor( `

`G` [, `factorisations`] )

`IsTensor`

takes as input a matrix group `G` and seeks to decide whether
or not `G` preserves a non-trivial tensor decomposition of the underlying
vector space.

The implementation currently demands that `G` acts irreducibly, although
this is not an inherent requirement of the algorithm.

The second optional argument is a list of possible factorisations of *d*,
the dimension of *G*. For each *[r, s]* in this list where *rs = d*, the
function seeks to decide whether `G` preserves a non-trivial tensor
decomposition of the underlying space as the tensor product of two spaces
of dimensions *r* and *s*.

The function returns a list containing three values: a boolean, a
*G*-module record, `module`, for *G*, and a change-of-basis matrix which
exhibits the decomposition (if one is found). If the boolean is `false`

,
then `G` is not a tensor product. If the boolean is `true`

, then `G` is
a tensor product and the second argument in the list are the two tensor
factors.

If `IsTensor`

cannot decide whether `G` or not preserves a tensor
decomposition, then it returns `"unknown"`

. The second entry returned
is now the list of unresolved tensor factorisations.

gap> ReadDataPkg ("matrix", "data", "a5xa5d25.gap"); gap> x:=IsTensor (G);; gap> x[1]; true gap> #Hence we have found a tensor decomposition. gap> #Set up the two factors gap> U := x[2][1];; gap> W := x[2][2];; gap> DisplayMat (GeneratorsFlag (U)); 4 1 5 2 4 5 4 3 6 2 2 2 4 5 6 . 1 5 6 4 5 2 6 3 . . 5 1 4 2 1 4 4 5 . 3 3 6 5 4 6 5 6 3 3 . 4 1 2 1 3 1 3 2 6 1 4 2 6 3 . . 4 . . 5 4 2 3 2 4 1 6 4 4 6 3 1 6 6 6 3 5 1 4 3 3 5 1 . 2 6 2 1 2 4 4 . 4 6 gap> ReadDataPkg ("matrix", "data", "a5d4.gap"); gap> x := IsTensor (G); [ false, [ ], "undefined" ] gap> #Hence not a tensor product

The algorithm is described in [8, 9]. Since a complete implementation
requires basic tools which are not yet available in **GAP3**, the
performance of this function is **currently seriously limited**.

`KroneckerFactors( `

`g`, `d1`, `d2` [,`F`] )

`KroneckerFactors`

decides whether or not a matrix `g` can be written as
the Kronecker product of two matrices *A* and *B* of dimension `d1` and
`d2`, respectively. If the field *F* is not supplied, it is taken to be
`Field (Flat (`

. The function returns either the pair [`g`))*A*, *B*] or
`false`

.

`SmashGModule( `

`module`, `S` [,`flag`] )

`SmashGModule`

seeks to find a decomposition of a *G*-module with respect
to a normal subgroup of *G*.

`module` is a module for a finite group *G* of matrices over a finite
field and `S` is a set of matrices, generating a subgroup of *G*.

`SmashGModule`

attempts to find some way of decomposing the module with
respect to the normal subgroup *⟨ S ⟩ ^{G}*. It returns

`true`

if some decomposition is found, `false`

otherwise.
It first ensures that *G* acts absolutely irreducibly and that `S`
contain at least one non-scalar matrix. If either of these conditions
fails, then it returns `false`

. The function returns `true`

if it
succeeds in verifying that either *G* acts imprimitively, or
semilinearly, or preserves a tensor product, or preserves a symmetric
tensor product (that is, permutes the tensor factors) or *G* normalises a
group which is extraspecial or a 2-group of symplectic type. Each of
these decompositions, if found, involves *⟨ S ⟩ ^{G}* in a
natural way. Components are added to the record

`false`

. In general, the answer `false`

indicates
that there is no such decomposition with respect to `SmashGModule`

may fail to find a symmetric tensor
product decomposition, since the detection of such a decomposition relies
on the choice of random elements.
`SmashGModule`

adds conjugates to `S` until a decomposition of the
underlying vector space as a sum of irreducible *⟨
S⟩*-modules is found. The functions `SemiLinearDecomposition`

,
`TensorProductDecomposition`

, `SymTensorProductDecomposition`

, and
`ExtraSpecialDe\-composition`

now search for decompositions.

At the end of the call to `SmashGModule`

, `S` may be larger than at the
start (but its normal closure has not changed).

The only permitted value for the optional parameter `flag` is the string
`"PartialSmash"`

. If `"PartialSmash"`

is supplied, `SmashGModule`

returns `false`

as soon as it is clear that *G* is not the normaliser of
a *p*-group nor does it preserve a symmetric tensor product decomposition
with respect to *⟨ S ⟩ ^{G}*.

The algorithm is described in [6].

`HomGModule( `

`module1`, `module2` )

This function can only be run if `IsIrreducible(`

has returned
`module1`)`true`

. `module1` and `module2` are assumed to be *G*-modules for the
same group *G*, and a basis of the space of *G*-homomorphisms from
`module1` to `module2` is calculated and returned. Each homomorphism in
this list is given as a *d _{1} × d_{2}* matrix, where

`IsomorphismGModule( `

`module1`, `module2` )

This function tests the *G*-modules `module1` and `module2` for
isomorphism. Both *G*-modules must be defined over the same field with
the same number of defining matrices, and at least one of them must be
known to be irreducible (that is, `IsIrreducible(`

has returned
`module`)`true`

). Otherwise the function will exit with an error. If they are
not isomorphic, then `false`

is returned. If they are isomorphic, then a
*d × d* matrix is returned (where *d* is the dimension of the
modules) whose rows give the images of the standard basis vectors of
`module1` in an isomorphism to `module2`.

The algorithm is described in [5].

`CompositionFactors( `

`module` )

`module` is a *G*-module. This function returns a list, each element of
which is itself a 2-element list [`mod`, `int`], where `mod` is an
irreducible composition factor of `module`, and `int` is the multiplicity
of this factor in `module`. The elements `mod` correspond to
non-isomorphic irreducible modules.

**Example 1**

gap> # First set up the natural permutation module for the gap> # alternating groupAover the field_{5}GF(2). gap> P := Group ((1,2,3), (3,4,5));; gap> M := PermGModule (P, GF(2)); rec( field := GF(2), dimension := 5, generators := [ [ [ 0*Z(2), Z(2)^0, 0*Z(2), 0*Z(2), 0*Z(2) ], [ 0*Z(2), 0*Z(2), Z(2)^0, 0*Z(2), 0*Z(2) ], [ Z(2)^0, 0*Z(2), 0*Z(2), 0*Z(2), 0*Z(2) ], [ 0*Z(2), 0*Z(2), 0*Z(2), Z(2)^0, 0*Z(2) ], [ 0*Z(2), 0*Z(2), 0*Z(2), 0*Z(2), Z(2)^0 ] ], [ [ Z(2)^0, 0*Z(2), 0*Z(2), 0*Z(2), 0*Z(2) ], [ 0*Z(2), Z(2)^0, 0*Z(2), 0*Z(2), 0*Z(2) ], [ 0*Z(2), 0*Z(2), 0*Z(2), Z(2)^0, 0*Z(2) ], [ 0*Z(2), 0*Z(2), 0*Z(2), 0*Z(2), Z(2)^0 ], [ 0*Z(2), 0*Z(2), Z(2)^0, 0*Z(2), 0*Z(2) ] ] ], isGModule := true ) gap> # Now test for irreducibility, and calculate a proper submodule. gap> IsIrreducible (M); false gap> SM := SubGModule (M, SubbasisFlag (M));; gap> DimensionFlag (SM); 4 gap> DSM := DualGModule (SM);; gap> # Test to see if SM is self-dual. We must prove irreducibility first. gap> IsIrreducible (SM); true gap> IsAbsolutelyIrreducible (SM); true gap> IsomorphismGModule (SM, DSM); [ [ 0*Z(2), Z(2)^0, Z(2)^0, 0*Z(2) ], [ Z(2)^0, 0*Z(2), Z(2)^0, 0*Z(2) ], [ Z(2)^0, Z(2)^0, 0*Z(2), Z(2)^0 ], [ 0*Z(2), 0*Z(2), Z(2)^0, 0*Z(2) ] ] gap> # This is an explicit isomorphism. gap> # Now form a tensor product and decompose it into composition factors. gap> TM := TensorProductGModule (SM, SM);; gap> cf := CompositionFactors (TM);; gap> Length (cf); 3 gap> DimensionFlag(cf[1][1]); cf[1][2]; 1 4 gap> DimensionFlag(cf[2][1]); cf[2][2]; 4 2 gap> DimensionFlag(cf[3][1]); cf[3][2]; 4 1 gap> # This tells us that TM has three composition factors, of dimensions gap> # 1, 4 and 4, with multiplicities 4, 2 and 1, respectively. gap> # Is one of the 4-dimensional factors isomorphic to TM? gap> IsomorphismGModule (SM, cf[2][1]); false gap> IsomorphismGModule (SM, cf[3][1]); [ [ 0*Z(2), Z(2)^0, 0*Z(2), 0*Z(2) ], [ 0*Z(2), 0*Z(2), 0*Z(2), Z(2)^0 ], [ Z(2)^0, 0*Z(2), Z(2)^0, 0*Z(2) ], [ 0*Z(2), 0*Z(2), Z(2)^0, 0*Z(2) ] ] gap> IsAbsolutelyIrreducible (cf[2][1]); false gap> DegreeFieldExtFlag(cf[2][1]); 2 gap> # If we extend the field of cf[2][1] toGF(4), it should gap> # become reducible. gap> MM := GModule (GeneratorsFlag (cf[2][1]), GF(4));; gap> CF2 := CompositionFactors (MM);; gap> Length (CF2); 2 gap> DimensionFlag (CF2[1][1]); CF2[1][2]; 2 1 gap> DimensionFlag (CF2[2][1]); CF2[2][2]; 2 1 gap> # It reduces into two non-isomorphic 2-dimensional factors.

In the next example, we investigate the structure of a matrix group using
`SmashGModule`

and access some of the stored information about the
computed decomposition.

**Example 2**

gap> a := [ > [0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], > [0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0], > [1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], > [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0], > [0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0], > [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0], > [0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0], > [0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0], > [0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0], > [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0], > [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0], > [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1]] * Z(2)^0;; gap> b := [ > [1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0], > [0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0], > [0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1], > [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], > [0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], > [0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0], > [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0], > [0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0], > [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0], > [0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0], > [0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0], > [0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0]] * Z(2)^0;; gap> c := [ > [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], > [0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0], > [0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0], > [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0], > [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0], > [0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0], > [0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0], > [0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0], > [0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0], > [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0], > [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1], > [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1]] * Z(2)^0;; gap> gens := [a, b, c];; gap> # Next we define the module. gap> M := GModule (gens);; gap> # So far only the basic components have been set. gap> RecFields (M); [ "field", "dimension", "generators"", "isGModule" ] gap> gap> # First we check for irreducibility and absolute irreducibility. gap> IsIrreducible (M); true gap> IsAbsolutelyIrreducible (M); true gap> # A few more components have been set during these two function calls. gap> RecFields(M); [ "field", "dimension", "generators"", "isGModule", "algEl", "algElMat", "algElCharPol", "algElCharPolFac", "algElNullspaceVec", "algElNullspaceDim", "reducible", "degreeFieldExt", "absolutelyReducible" ] gap> # The function Commutators forms the list of commutators of generators. gap> S := Commutators(gens);; gap> InfoSmash := Print;; gap> # Setting InfoSmash to Print means that SmashGModule prints out gap> # intermediate output to tell us what it is doing. If we gap> # read this output it tells us what kind of decomposition SmashGModule gap> # has found. Otherwise the output is only a true or false. gap> # All the relevant information is contained in the components of M. gap> SmashGModule (M, S); Starting call to SmashGModule. At top of main SmashGModule loop, S has 2 elements. Translates of W are not modules. At top of main SmashGModule loop, S has 3 elements. Translates of W are not modules. At top of main SmashGModule loop, S has 4 elements. Translates of W are not modules. At top of main SmashGModule loop, S has 5 elements. Group embeds in GammaL(4, GF(2)^3). SmashGModule returns true. true gap> # Additional components are set during the call to SmashGModule. gap> RecFields(M); [ "field", "dimension", "generators", "isGModule", "algEl", "algElMat", "algElCharPol", "algElCharPolFac", "algElNullspaceVec", "algElNullspaceDim", "reducible", "degreeFieldExt", "absolutelyReducible", "semiLinear", "linearPart", "centMat", "frobeniusAutomorphisms" ] gap> SemiLinearFlag (M); true gap> # This flag tells us G that acts semilinearly. gap> DegreeFieldExtFlag (M); 3 gap> #This flag tells us the relevant extension field is GF(2\^3) gap> Length (LinearPartFlag (M)); 5 gap> # LinearPartFlag (M) is a set of normal subgroup generators for the gap> # intersection of G with GL(4, GF(2\^3)). It is also the contents of S gap> # at the end of the call to SmashGModule and is bigger than the set S gap> # which was input since conjugates have been added. gap> FrobeniusAutomorphismsFlag (M); [ 0, 0, 1 ] gap> # The first two generators of G act linearly, the last induces the field gap> # automorphism which maps x to x\^2 (= x\^(2\^1)) on GF(2\^3)

In our final example, we demonstrate how to test whether a matrix group is primitive and also how to select pseudo-random elements.

**Example 3**

gap> # Read in 18-dimensional representation of L(2, 17) over GF(41). gap> ReadDataPkg ("matrix", "data", "l217.gap"); gap> # Initialise a seed for random element generation. gap> InitPseudoRandom (G, 10, 100);; gap> # Now select a pseudo-random element. gap> g := PseudoRandom (G);; gap> OrderMat (g); 3 gap> h := ElementOfOrder (G, 8, 10);; gap> OrderMat (h); 8 gap> #Is the group primitive? gap> R := IsPrimitive(G);; gap> #Examine the boolean returned. gap> R[1]; false gap> M := R[2];; gap> #What is the block system found? gap> BlockSystemFlag (M); rec( nmrBlocks := 18, block := [ [ 0*Z(41), 0*Z(41), 0*Z(41), 0*Z(41), 0*Z(41), 0*Z(41), 0*Z(41), 0*Z(41), 0*Z(41), 0*Z(41), 0*Z(41), 0*Z(41), 0*Z(41), 0*Z(41), 0*Z(41), Z(41)^0, 0*Z(41), 0*Z(41) ] ], maps := [ 1, 2, 3 ], permGroup := Group( ( 1, 2)( 3, 7)( 5,11)( 6,12)( 8,10)(13,14)(15,17) (16,18), ( 1, 3, 8,11,15, 9,13, 7,12,16, 6, 2, 5, 4,10,14,17), ( 1, 4, 2, 6, 3, 9, 7,12)( 5, 8,10,11,13,17,15,14) ), isBlockSystem := true ) gap> v := > [ 0*Z(41), 0*Z(41), 0*Z(41), 0*Z(41), 0*Z(41), 0*Z(41), 0*Z(41), > 0*Z(41), 0*Z(41), 0*Z(41), 0*Z(41), 0*Z(41), 0*Z(41), 0*Z(41), > 0*Z(41), Z(41)^0, 0*Z(41), 0*Z(41) ];; gap> #Illustrate use of MinBlocks gap> B := MinBlocks (M, [v]);; gap> B; rec( nmrBlocks := 18, block := [ [ 0*Z(41), 0*Z(41), 0*Z(41), 0*Z(41), 0*Z(41), 0*Z(41), 0*Z(41), 0*Z(41), 0*Z(41), 0*Z(41), 0*Z(41), 0*Z(41), 0*Z(41), 0*Z(41), 0*Z(41), Z(41)^0, 0*Z(41), 0*Z(41) ] ], maps := [ 1, 2, 3 ], permGroup := Group( ( 1, 2)( 3, 7)( 5,11)( 6,12)( 8,10)(13,14)(15,17) (16,18), ( 1, 3, 8,11,15, 9,13, 7,12,16, 6, 2, 5, 4,10,14,17), ( 1, 4, 2, 6, 3, 9, 7,12)( 5, 8,10,11,13,17,15,14) ), isBlockSystem := true )

`ClassicalForms( `

`G` )

Given as input, a classical, irreducible group `G`, `ClassicalForms`

will
try to find an invariant classical form for `G` (that is, an invariant
symplectic or unitary bilinear form or an invariant symmetric bilinear
form together with an invariant quadratic form, invariant modulo scalars
in each case) or try to prove that no such form exists. The dimension of
the underlying vector space must be at least 3.

The function may find a form even if `G` is a proper subgroup of a
classical group; however, it is likely to fail for subgroups of
*ΓL*. In these cases "unknown" (see below) is returned.

The results "linear", "symplectic", "unitary", "orthogonal..."
and "absolutely reducible" are always correct, but "unknown" can
either imply that the algorithm failed to find a form and it could not
prove the linear case **or** that `G` is not a classical group.

- [ "unknown" ]:

it is not known if`G`fixes a form.

- [ "unknown", "absolutely reducible" ]:

`G`acts absolutely reducible on the underlying vector space. The function does not apply in this case.

- [ "linear" ]:

it is known that`G`does not fix a classical form modulo scalars.

- [ "symplectic",
`form`,`scalars`]:

`G`fixes a symplectic`form`modulo`scalars`. The form is only unique up to scalar multiplication. In characteristic two this also implies that no quadratic form is fixed.

- [ "unitary",
`form`,`scalars`]:

`G`fixes a unitary`form`modulo`scalars`. The form is only unique up to scalar multiplication.

[ "orthogonalcircle", `form`, `scalars`, `quadratic`, ... ]

[ "orthogonalplus", `form`, `scalars`, `quadratic`, ... ]

- [ "orthogonalminus",
`form`,`scalars`,`quadratic`, ... ]:

`G`fixes a orthogonal`form`with corresponding`quadratic`form modulo`scalars`. The forms are only unique up to scalar multiplication.

The function might return **more** than one list. However, in
characteristic 2 it will **not** return "symplectic" if `G` fixes a
quadratic form.

A bilinear form is returned as matrix *F* such that *g * F * g ^{tr}*
equals

\vbox

gap> G := O( 0, 9, 9 ); gap> f := ClassicalForms(G);; gap> Q := f[1][4];; gap> DisplayMat(Q); . 1 . . . . . . . . . . . . . . . . . . 1 . . . . . . . . . 1 . . . . . . . . . 1 . . . . . . . . . 1 . . . . . . . . . 1 . . . . . . . . . 1 . . . . . . . . . 1

\vbox

gap> DisplayMat( G.1 * Q * TransposedMat(G.1) ); . 1 . . . . . . . . . . . . . . . . . . 1 . . . . . . . . . 1 . . . . . . . . . 1 . . . . . . . . . 1 . . . . . . . . . 1 . . . . . . . . . 1 . . . . . . . . . 1

\vbox

gap> DisplayMat( G.2 * Q * TransposedMat(G.2) ); . . . . . . . . . 1 . . . . . . . 1 . . 1 . . . . . . . . . 1 . . . . . . . . . 1 . . . . . . . . . 1 . . . . . . . . . 1 . . . . . . . . . 1 . . 2 . . . . . . 1

Note that in general ` g * Q * TransposedMat(g) `

is not equal to ` Q `

for an element of an orthogonal group because you have to normalise the
quadratic form such that it is an upper triangular matrix. In the above
example for `G.1` you have to move the *1* in position *(9,2)* to
position *(2,9)* adding it to the *2* which gives a *0*, and you have to
move the *2* in position *(1,2)* to position *(2,1)* thus obtaining the
original quadratic form.

**Examples**

gap> G := SP( 4, 2 ); SP(4,2) gap> ClassicalForms( G ); [ [ "symplectic", [ [ 0*Z(2), 0*Z(2), 0*Z(2), Z(2)^0 ], [ 0*Z(2), 0*Z(2), Z(2)^0, 0*Z(2) ], [ 0*Z(2), Z(2)^0, 0*Z(2), 0*Z(2) ], [ Z(2)^0, 0*Z(2), 0*Z(2), 0*Z(2) ] ], [ Z(2)^0, Z(2)^0 ] ] ]

In this case `G` leaves a symplectic (and symmetric) form invariant but
does not fix a quadratic form.

gap> G := O( -1, 4, 2 ); O(-1,4,2) gap> ClassicalForms( G ); [ [ "orthogonalminus", [ [ 0*Z(2), Z(2)^0, 0*Z(2), 0*Z(2) ], [ Z(2)^0, 0*Z(2), 0*Z(2), 0*Z(2) ], [ 0*Z(2), 0*Z(2), 0*Z(2), Z(2)^0 ], [ 0*Z(2), 0*Z(2), Z(2)^0, 0*Z(2) ] ], [ Z(2)^0, Z(2)^0 ], [ [ 0*Z(2), Z(2)^0, 0*Z(2), 0*Z(2) ], [ 0*Z(2), 0*Z(2), 0*Z(2), 0*Z(2) ], [ 0*Z(2), 0*Z(2), Z(2)^0, Z(2)^0 ], [ 0*Z(2), 0*Z(2), 0*Z(2), Z(2)^0 ] ] ] ]

In this case `G` leaves a symplectic and symmetric form invariant and
there exists also an invariant quadratic form.

gap> m1 := > [ [ Z(2^2), Z(2)^0, 0*Z(2), Z(2^2) ], > [ Z(2^2)^2, Z(2^2), Z(2^2)^2, Z(2^2) ], > [ 0*Z(2), Z(2^2)^2, Z(2^2)^2, Z(2)^0 ], > [ Z(2^2), Z(2^2)^2, Z(2^2), Z(2^2)^2 ] ];; gap> m2 := > [ [ 0*Z(2), 0*Z(2), 0*Z(2), Z(2^2) ], > [ 0*Z(2), 0*Z(2), Z(2^2)^2, 0*Z(2) ], > [ 0*Z(2), Z(2^2)^2, 0*Z(2), Z(2^2) ], > [ Z(2^2), 0*Z(2), Z(2^2)^2, 0*Z(2) ] ];; gap> G := Group( m1, m2 );; gap> ClassicalForms( G ); [ [ "unknown" ], [ "symplectic", [ [ 0*Z(2), Z(2)^0, Z(2)^0, Z(2^2)^2 ], [ Z(2)^0, 0*Z(2), Z(2^2), Z(2)^0 ], [ Z(2)^0, Z(2^2), 0*Z(2), Z(2)^0 ], [ Z(2^2)^2, Z(2)^0, Z(2)^0, 0*Z(2) ] ], [ Z(2)^0, Z(2)^0 ] ] ]

The "symplectic" indicates that an invariant symplectic form exists, the "unknown" indicates that an invariant "unitary" form might exist. Using the test once more, one gets:

gap> ClassicalForms( G ); [ [ "symplectic", [ [ 0*Z(2), Z(2^2)^2, Z(2^2)^2, Z(2^2) ], [ Z(2^2)^2, 0*Z(2), Z(2)^0, Z(2^2)^2 ], [ Z(2^2)^2, Z(2)^0, 0*Z(2), Z(2^2)^2 ], [ Z(2^2), Z(2^2)^2, Z(2^2)^2, 0*Z(2) ] ], [ Z(2)^0, Z(2)^0 ] ], [ "unitary", [ [ 0*Z(2), 0*Z(2), 0*Z(2), Z(2)^0 ], [ 0*Z(2), 0*Z(2), Z(2)^0, 0*Z(2) ], [ 0*Z(2), Z(2)^0, 0*Z(2), 0*Z(2) ], [ Z(2)^0, 0*Z(2), 0*Z(2), 0*Z(2) ] ], [ Z(2)^0, Z(2)^0 ] ] ]

So `G` indeed fixes both a symplectic and unitary form but no quadratic
form.

gap> ReadDataPkg ("matrix", "data", "a5d4.gap"); gap> ClassicalForms( G ); [ [ "unknown", "absolutely reducible" ] ]

`G` acts irreducibly, however `ClassicalForms`

is not able to check if an
invariant bilinear or quadratic form exists.

gap> ReadDataPkg ("matrix", "data", "a5d5.gap" ); gap> ClassicalForms( G ); [ [ "unknown" ] ] gap> IsAbsolutelyIrreducible(GModule(G)); true

Although `G` fixes a symmetric form, `ClassicalForms`

is not able to find
an invariant form because `G` is not a classical group.

`RecogniseClassical( `

`G` [,`strategy`] [,`case`] [,`N`] )

`RecogniseClassical`

takes as input a group `G`, which is a subgroup of
GL*(d,q)* with *d > 1*, and seeks to decide whether or not *G* contains
a classical group in its natural representation over a finite field.

`strategy` is one of the following:

- "clg":

use the algorithm of Celler and Leedham-Green [3].

- "np":

use the algorithm of Niemeyer and Praeger [11, 12].

The default strategy is "clg".

The parameter `case` is used to supply information about the specific
non-degenerate bilinear, quadratic or sesquilinear forms on the
underlying vector space *V* preserved by *G* modulo scalars. The value
of `case` must be one of the following:

- "all":

`RecogniseClassical`

will try to determine the case of`G`. This is the default.

- "linear":

`G`*≤*GL*(d,q),*and preserves no non-degenerate bilinear, quadratic or sesquilinear form on*V.*Set*Ω :=*SL*(d,q).*

- "symplectic":

`G`*≤ \GSp(d,q),*with*d*even, and if*q*is also even we assume that`G`preserves no non-degenerate quadratic form on*V.*Set*Ω := \Sp(d,q).*

- "orthogonalplus":

`G`*≤*GOand^{+}(d,q)*d*is even. Set*Ω := Ω*^{+}(d,q).

- "orthogonalminus":

`G`*≤*GOand^{-}(d,q)*d*is even. Set*Ω := Ω*^{-}(d,q).

- "orthogonalcircle":

`G`*≤*GOand^{o}(d,q)*d*is odd. Set*Ω := Ω*^{o}(d,q).

- "unitary":

`G`*≤ \GU(d,q)*, where*q*is a square. Set*Ω := \SU(d,q)*.

`N` is a positive integer which determines the number of random elements
selected. Its default value depends on the strategy and case; see
RecogniseClassicalCLG and RecogniseClassicalNP for additional
details.

In summary, the aim of `RecogniseClassical`

is to test whether `G`
contains the subgroup *Ω* corresponding to the value of `case`.

The function returns a record whose contents depends on the strategy
chosen. Detailed information about components of this record can be
found in RecogniseClassicalCLG and RecogniseClassicalNP. However,
the record has certain **common** components **independent** of the strategy
and these can be accessed using the following flag functions.

`ClassicalTypeFlag`

:

returns "linear", "symplectic", "orthogonalplus", "orthogonalminus", "orthogonalcircle" or "unitary" if`G`is known to be a classical group of this type modulo scalars, otherwise "unknown". Note that*\Sp(2,q)*is isomorphic to SL*(2,q)*; "linear" not "symplectic" is returned in this case.

`IsSLContainedFlag`

:

returns`true`

if`G`contains the special linear group SL*(d,q)*.

`IsSymplecticGroupFlag`

:

returns`true`

if`G`is contained in*\GSp(d,q)*modulo scalars and contains*\Sp(d,q)*.

`IsOrthogonalGroupFlag`

:

returns`true`

if`G`is contained in an orthogonal group modulo scalars and contains the corresponding*Ω*.

`IsUnitaryGroupFlag`

:

returns`true`

if`G`is contained in an unitary group modulo scalars and contains the corresponding*Ω*.

These last four functions return `true`

, `false`

, or "unknown". Both
`true`

and `false`

are **conclusive**. The answer "unknown" indicates
either that the algorithm **failed** to determine whether or not `G` is a
classical group or that the algorithm is not applicable to the supplied
group; see RecogniseClassicalCLG and RecogniseClassicalNP for
additional details.

If `RecogniseClassical`

**failed** to prove that `G` is a classical group,
additional information about the possible Aschbacher categories of `G`
might have been obtained. See RecogniseClassicalCLG for details.

**Example 1**

gap> G := SL(7, 5); SL(7,5) gap> r := RecogniseClassical( G, "clg" );; gap> ClassicalTypeFlag(r); "linear" gap> IsSLContainedFlag(r); true gap> r := RecogniseClassical( G, "np" );; gap> ClassicalTypeFlag(r); "linear" gap> IsSLContainedFlag(r); true

**Example 2**

gap> ReadDataPkg ("matrix", "data", "j1.gap" ); gap> DisplayMat(GeneratorsFlag(G)); 9 1 1 3 1 3 3 1 1 3 1 3 3 9 1 3 1 3 3 9 1 3 1 3 3 9 1 1 1 3 3 9 1 1 3 3 3 9 1 1 3 1 3 9 1 1 3 1 3 . 1 . . . . . . . 1 . . . . . . . 10 . . . . . . . 1 . . . . . . . 10 . . . . . . . 10 10 . . . . . . gap> r := RecogniseClassical( G, "clg" );; gap> ClassicalTypeFlag(r); "unknown"

The algorithms are described in [3, 11, 12].

In this section, we describe functions developed by Celler and Leedham-Green (see [4] for details) to recognise constructively classical groups in their natural representation over finite fields.

`ConstructivelyRecogniseClassical( `

`G`, "linear" )

computes both a standard generating set for a matrix group `G` which
contains the **special linear group** and expressions for the new
generators in terms of

. This generating set will allow
you to write an element of `G`.generators`G` as a word in the **given** generating set of
`G`.

The algorithm is of polynomial complexity in the dimension and field
size. However, it is a **Las Vegas algorithm**, i.e. there is a chance
that the algorithm fails to complete in the expected time. It will run
**indefinitely** if `G` does not contain the special linear group.

The following functions can be applied to the record `sl`

returned.

`SizeFlag( `

`sl` )

returns the size of `G`.

`Rewrite( `

`sl`, `elm` )

returns an expression such that `Value( Rewrite( `

is equal to the element `sl`, `elm` ),
`G`.generators )`elm`.

**Example**

gap> m1 := [ [ 0*Z(17), Z(17), Z(17)^10, Z(17)^12, Z(17)^2 ], > [ Z(17)^13, Z(17)^10, Z(17)^15, Z(17)^8, Z(17)^0 ], > [ Z(17)^10, Z(17)^6, Z(17)^9, Z(17)^8, Z(17)^10 ], > [ Z(17)^13, Z(17)^5, Z(17)^0, Z(17)^12, Z(17)^5 ], > [ Z(17)^14, Z(17)^13, Z(17)^5, Z(17)^10, Z(17)^0 ] ];; gap> m2 := [ [ 0*Z(17), Z(17)^10, Z(17)^2, 0*Z(17), Z(17)^10 ], > [ 0*Z(17), Z(17)^6, Z(17)^0, Z(17)^4, Z(17)^15 ], > [ Z(17)^7, Z(17)^6, Z(17)^10, Z(17), Z(17)^2 ], > [ Z(17)^3, Z(17)^10, Z(17)^5, Z(17)^4, Z(17)^6 ], > [ Z(17)^0, Z(17)^8, Z(17)^0, Z(17)^5, Z(17) ] ];; gap> G := Group( m1, m2 );; gap> sl := ConstructivelyRecogniseClassical( G, "linear" );; gap> SizeFlag(sl); 338200968038818404584356577280 gap> w := Rewrite( sl, m1^m2 );; gap> Value( w, [m1,m2] ) = m1^m2; true

The algorithm is described in [4].

`RecogniseMatrixGroup( `

`G` )

`RecogniseMatrixGroup`

attempts to recognise at least one of the
Aschbacher categories in which the matrix group `G` lies. It then
attempts to use features of this category to determine the order of `G`
and provide a membership test for `G`.

The algorithm is described in [13]. This implementation is **experimental**
and **limited** in its application; its inclusion in the package at this
time is designed primarily to illustrate the basic features of the
approach.

Currently the function attempts to recognise groups that are reducible, imprimitive, tensor products or classical in their natural representation.

The function returns a record whose components store detailed information
about the decomposition of `G` that it finds. The record contents can be
viewed using `DisplayMatRecord`

.

The record consists of **layers** of records which are the kernels at the
various stages of the computation. Individual layers are accessed via the
component .kernel. We number these layers 1 to *n* where layer 0 is
`G`. The n-th layer is a *p*-group generated by lower uni-triangular
matrices. Information about this *p*-group is stored in the component
.pGroup. At the i-th layer (*1 ≤ i ≤ n*) we have a group generated
by matrices with at most *i-1* identity blocks down the diagonal,
followed by a non-singular block. Below the blocks we have non-zero
entries and above them we have zero entries. Call this group *G _{i}* and
the group generated by the non-singular block on the diagonal

The central part of `RecogniseMatrixGroup`

is the recursive function
`GoDownChain`

which takes as arguments a record and a list of matrices.
`RecogniseMatrixGroup`

initialises this record and then calls
`GoDownChain`

with the record and a list of the generators of `G`.

Assume we pass `GoDownChain`

the i-th layer of our record and a list of
matrices (possibly empty) in the form described above.

If the i-th layer is the last, then we construct a power-commutator presentation for the group generated by the list of matrices.

Otherwise, we now check if we have already decomposed *T _{i}*. If not, we
split the module for

`IsIrreducible`

. We set .quotient to be
the trivial group of dimension that of the irreducible submodule, and we
store the basis-change matrix. We also initialise the next layer of our
record, which will correspond to the kernel of the homomorphism from
`GoDownChain`

with the layer and the
list of matrices we started with.
If we have a decomposition for *T _{i}*, then we apply the basis-change
stored in our record to the list of matrices and decide whether the new
matrices preserve the decomposition. If they do not, then we discard the
current decomposition of

`GoDownChain`

.
If the matrices preserve the decomposition, then we extract the blocks in
the matrices which correspond to `.quotient`

. We decide if these blocks
lie in `.quotient`

.

If the blocks lie in `.quotient`

, then the next step is to construct
relations on `.quotient`

which we will then evaluate on the generators of
*G _{i}* to put into the next layer. There are two approaches to
constructing relations on

`.quotient`

. Let `.quotient`

. We construct a permutation
representation on `.quotient`

. The first approach is to take the image of
an element of `.quotient`

in the permutation group and then pull it back
to the permutation group. The second approach is to take a random word in
`.quotient`

contains SL, then we use special techniques.
If the list of matrices with which we called `GoDownChain`

is empty, then
we construct random relations on `.quotient`

, evaluate these in *G _{i}* to
get a new list of matrices and then call

`GoDownChain`

with this list and
the next layer of our record. We use parameters similar to those in the
Random Schreier-Sims algorithm to control how hard we work.
If the list of matrices is non-empty, then we take generator relations on
the list of blocks and evaluate these in *G _{i}*. This gives us a new list
of matrices and we call

`GoDownChain`

with the list and the next layer of
our record.
If, in evaluating the relations in *G _{i}*, we get a non-identity block,
then we deduce that our permutation representation is not faithful. In
this case, the next layer corresponds to the kernel of the action that
provided the representation.

If these blocks do not lie in `.quotient`

, then we have to enlarge it. We
then try to find out the Aschbacher category in which `.quotient`

lies,
and its size. After applying these tests and computing the size we then
construct generator relations on the list of generators of `.quotient`

and put them into the kernel. We then call `GoDownChain`

with our record
and an empty list of matrices.

We first test whether `.quotient`

is a classical group in its natural
representation using `RecogniseClassicalNP`

. If `.quotient`

contains SL,
we use `Constructively\-Recognise\-Classical`

to obtain both its size and
a membership test; if `.quotient`

contains one of the other classical
groups, we simply report this. If `.quotient`

contains a classical
group, we terminate the testing. If `RecogniseClassicalNP`

returns
`false`

, then we call `RecogniseClassicalCLG`

. If `.quotient`

contains
one of the classical groups, then we behave as before. If `.quotient`

is
not a classical group, then we obtain a list of possibilities for
`.quotient`

. This list may help to rule out certain Aschbacher
categories and will give pointers to the ones which we should investigate
further.

If `.quotient`

might be imprimitive, then we test this using
`IsPrimitive`

. If `.quotient`

is imprimitive, then we obtain a
permutation representation for the action on the blocks and we store this
in `.quotient`

. We set the size of `.quotient`

to be the size of the
permutation group. If the action is not faithful, then we compute the
kernel of the action at the next layer and then we have the correct size
for `.quotient`

. If `.quotient`

is imprimitive, then the testing ends
here. If `IsPrimitive`

returns `unknown`

or `true`

, then we store this in
`.quotient`

. We then reprocess `.quotient`

using `RecogniseClassicalCLG`

.

If `.quotient`

might be a tensor product, then we test this using
`IsTensor`

. If `.quotient`

is a tensor product, then we store the tensor
factors in `.quotient`

. Currently, we do not exploit this conclusion . If
`IsTensor`

returns `unknown`

or `false`

then we store this in
`.quotient`

. We then reprocess `.quotient`

using `RecogniseClassicalCLG`

.

By default, we obtain the size of `.quotient`

using
`PermGroupRepresentation`

. We advise the user if the list returned by
`RecogniseClassicalCLG`

suggests that the group contains an almost
simple group or an alternating group. `PermGroupRepresentation`

constructs a faithful permutation representation for `.quotient`

and we
store this in `.quotient`

.

We illustrate some of these features in the following example.
Additional examples can be found in `matrix/reduce/examples.tex`

.

gap> # Construct the group SL(2, 3) x SP(4, 3) gap> G1 := SL(2, 3);; gap> G2 := SP(4, 3);; gap> m1 := DiagonalMat_mtx( GF(3), G1.1, G2.1 );; gap> m2 := DiagonalMat_mtx( GF(3), G1.2, G2.2 );; gap> # Put something in the bottom left hand corner to give us a p-group gap> m1[3][1] := Z(3)^0;; gap> m2[5][2] := Z(3);; gap> G := Group( [m1, m2], m1^0 );; gap> # Apply RecogniseMatrixGroup to G gap> x := RecogniseMatrixGroup( G );; #I Input group has dimension 6 over GF(3) #I Layer number 1: Type = "Unknown" #I Size = 1, # of matrices = 2 #I Computing the next quotient #I <new> acts non-trivially on the block of dim 6 #I Found a quotient of dim 2 #I Restarting after finding a decomposition #I Layer number 1: Type = "Perm" #I Size = 1, # of matrices = 2 #I Submodule is invariant under <new> #I Enlarging quotient, old size = 1 #I Is quotient classical? #I Dimension of group is <= 2, you must supply form #I The quotient contains SL #I New size = 24 #I Adding generator relations to the kernel #I Layer number 2: Type = "Unknown" #I Size = 1, # of matrices = 2 #I Computing the next quotient #I <new> acts non-trivially on the block of dim 4 #I Found a quotient of dim 4 #I Restarting after finding a decomposition #I Layer number 2: Type = "Perm" #I Size = 1, # of matrices = 2 #I Submodule is invariant under <new> #I Enlarging quotient, old size = 1 #I Is quotient classical? #I The case is symplectic #I This algorithm does not apply in this case. #I The quotient contains SP #W Applying Size to (matrix group) quotient #I New size = 51840 #I Adding generator relations to the kernel #I Restarting after enlarging the quotient #I Layer number 2: Type = "Perm" #I Size = 51840, # of matrices = 0 #I Using a permutation representation #I Adding random relations at layer number 2 #I Adding a random relation at layer number 2 #I Layer number 3: Type = "PGroup" #I Size = 1, # of matrices = 3 #I Reached the p-group case #I New size = 27 #I Adding a random relation at layer number 2 #I Adding a random relation at layer number 2 #I Kernel p-group, old size = 27 #I Kernel p-group, new size = 6561 #I Adding a random relation at layer number 2 #I Kernel p-group, old size = 6561 #I Kernel p-group, new size = 6561 #I Adding a random relation at layer number 2 #I Kernel p-group, old size = 6561 #I Kernel p-group, new size = 6561 #I Adding a random relation at layer number 2 #I Kernel p-group, old size = 6561 #I Kernel p-group, new size = 6561 #I Adding a random relation at layer number 2 #I Kernel p-group, old size = 6561 #I Kernel p-group, new size = 6561 #I Adding a random relation at layer number 2 #I Kernel p-group, old size = 6561 #I Kernel p-group, new size = 6561 #I Adding a random relation at layer number 2 #I Kernel p-group, old size = 6561 #I Kernel p-group, new size = 6561 #I Adding a random relation at layer number 2 #I Kernel p-group, old size = 6561 #I Kernel p-group, new size = 6561 #I Adding a random relation at layer number 2 #I Kernel p-group, old size = 6561 #I Kernel p-group, new size = 6561 #I Adding a random relation at layer number 2 #I Kernel p-group, old size = 6561 #I Kernel p-group, new size = 6561 #I Adding a random relation at layer number 2 #I Kernel p-group, old size = 6561 #I Kernel p-group, new size = 6561 #I Kernel is finished, size = 340122240 #I Restarting after enlarging the quotient #I Layer number 1: Type = "SL" #I Size = 8162933760, # of matrices = 0 #I Using the SL recognition #I Adding random relations at layer number 1 #I Adding a random relation at layer number 1 #I Layer number 2: Type = "Perm" #I Size = 340122240, # of matrices = 3 #I Submodule is invariant under <new> #I Using a permutation representation #I Adding generator relations to the kernel #I Kernel p-group, old size = 6561 #I Kernel p-group, new size = 6561 #I Adding a random relation at layer number 1 #I Layer number 2: Type = "Perm" #I Size = 340122240, # of matrices = 3 #I Submodule is invariant under <new> #I Using a permutation representation #I Adding generator relations to the kernel #I Kernel p-group, old size = 6561 #I Kernel p-group, new size = 6561 #I Adding a random relation at layer number 1 #I Layer number 2: Type = "Perm" #I Size = 340122240, # of matrices = 3 #I Submodule is invariant under <new> #I Using a permutation representation #I Adding generator relations to the kernel #I Kernel p-group, old size = 6561 #I Kernel p-group, new size = 6561 #I Adding a random relation at layer number 1 #I Layer number 2: Type = "Perm" #I Size = 340122240, # of matrices = 3 #I Submodule is invariant under <new> #I Using a permutation representation #I Adding generator relations to the kernel #I Kernel p-group, old size = 6561 #I Kernel p-group, new size = 6561 #I Adding a random relation at layer number 1 #I Layer number 2: Type = "Perm" #I Size = 340122240, # of matrices = 3 #I Submodule is invariant under <new> #I Using a permutation representation #I Adding generator relations to the kernel #I Kernel p-group, old size = 6561 #I Kernel p-group, new size = 6561 #I Adding a random relation at layer number 1 #I Layer number 2: Type = "Perm" #I Size = 340122240, # of matrices = 3 #I Submodule is invariant under <new> #I Using a permutation representation #I Adding generator relations to the kernel #I Kernel p-group, old size = 6561 #I Kernel p-group, new size = 6561 #I Adding a random relation at layer number 1 #I Layer number 2: Type = "Perm" #I Size = 340122240, # of matrices = 3 #I Submodule is invariant under <new> #I Using a permutation representation #I Adding generator relations to the kernel #I Kernel p-group, old size = 6561 #I Kernel p-group, new size = 6561 #I Adding a random relation at layer number 1 #I Layer number 2: Type = "Perm" #I Size = 340122240, # of matrices = 3 #I Submodule is invariant under <new> #I Using a permutation representation #I Adding generator relations to the kernel #I Kernel p-group, old size = 6561 #I Kernel p-group, new size = 6561 #I Adding a random relation at layer number 1 #I Layer number 2: Type = "Perm" #I Size = 340122240, # of matrices = 3 #I Submodule is invariant under <new> #I Using a permutation representation #I Adding generator relations to the kernel #I Kernel p-group, old size = 6561 #I Kernel p-group, new size = 6561 #I Adding a random relation at layer number 1 #I Layer number 2: Type = "Perm" #I Size = 340122240, # of matrices = 3 #I Submodule is invariant under <new> #I Using a permutation representation #I Adding generator relations to the kernel #I Kernel p-group, old size = 6561 #I Kernel p-group, new size = 6561 #I Kernel is finished, size = 8162933760 gap> # Let us look at what we have found gap> DisplayMatRecord( x ); #I Matrix group over field GF(3) of dimension 6 has size 8162933760 #I Number of layers is 3 gap> DisplayMatRecord( x, 1 ); #I Layer Number = 1 #I Type = SL #I Dimension = 2 #I Size = 24 gap> # The module for G splits into an irreducible submodule of dimension gap> # 2 and a quotient module of dimension 4. The restriction of G to gap> # the submodule contains SL(2, 3). Call this group G1. gap> DisplayMatRecord( x, 2 ); #I Layer Number = 2 #I Type = Perm #I Dimension = 4 #I Size = 51840 gap> # We have now taken relations on G1 and evaluated them in G to get gap> # a group H, which is the kernel of the homomorphism from G to G1. gap> # The group generated by the last 4x4 block on the diagonal of the gap> # matrices of H has an irreducible module and we have computed gap> # a permutation representation on it. Call this group H1. gap> DisplayMatRecord( x, 3 ); #I Layer Number = 3 #I Type = PGroup #I Dimension = 6 #I Size = 6561 gap> # We have now taken relations on H1 and evaluated them in H to get the gap> # kernel of the homomorphism from H to H1. This kernel consists of gap> # lower uni-triangular matrices. It is a p-group of size 6561.

In this section, we describe functions developed by Celler and Leedham-Green (see [3] for details) to recognise classical groups in their natural representation over finite fields.

`RecogniseClassicalCLG( `

`G` [,`case`] [,`N`] )

This is the top-level function, taking as input a group `G`, which is a
subgroup of GL*(d,q)* with *d > 1*. The other optional arguments have
the same meaning as those supplied to `RecogniseClassical`

. The default
value of `N`, the number of random elements to consider, depends on the
case; it is 40 for small fields and dimensions, but decreases to 10 for
larger dimensions.

**Constraints**

In the case of an orthogonal group, the dimension of the underlying
vector space must be at least 7, since there are exceptional isomorphisms
between the orthogonal groups in dimensions *6* or less and other
classical groups which are not dealt with in `RecogniseClassical\-CLG`

.
In dimension *8*, `RecognizeSO`

will **not** rule out the possibility of
*O _{7}(q)* embedded as irreducible subgroup of

`RecogniseClassicalCLG`

does The record returned by this function is similar to that described in RecogniseClassical. In particular, the flag functions described there and below can be applied to the record. You should ignore undocumented record components.

**Additional information**

`DualFormFlag`

:

if`G`has been proved to be a symplectic or orthogonal group,`DualFormFlag`

returns the symplectic or orthogonal form.

`QuadraticFormFlag`

:

if`G`has been proved to be an orthogonal group,`QuadraticFormFlag`

returns the quadratic form.

`UnitaryFormFlag`

:

if`G`has been proved to be a unitary group,`DualFormFlag`

returns the symplectic or orthogonal form.

If `RecogniseClassical`

**failed** to prove that `G` is a classical group,
additional information about the possible Aschbacher categories of `G`
might have been obtained.

In particular, the following flag functions may be applied to the record.
If one of these functions returns a list, it has the following meaning:
**if** `G` belongs to the corresponding Aschbacher category, **then** `G` is
determined by one of the possibilities returned; it does **not** imply that
`G` is a member of this category. However, an empty list indicates that
`G` does not belong to this category. Each of these functions may also
return "unknown".

A group `G` is **almost simple** if `G` contains a non-abelian simple group
*T* and is contained in the automorphism group of *T*. If `G` is almost
simple, then `G` is either an almost sporadic group, an almost
alternating group, or an almost Chevalley group.

`PossibleAlmostSimpleFlag`

:

if`G`is not a classical group, this function returns a list of possible almost sporadic groups modulo scalars. This function deals only with**sporadic**groups*T*. The names of the corresponding non-abelian simple groups are returned. Possible names are: "M11", "M12", "M22", "M23", "M24", "J2", "Suz", "HS", "McL", "Co3", "Co2", "Co1", "He", "Fi22", "Fi23", "F3+", "HN", "Th", "B", "M", "J1", "ON", "J3", "Ly", "Ru", "J4".

`PossibleAlternatingGroupsFlag`

:

if`G`is not a classical group, this function returns a list of possible almost alternating groups modulo scalars. This list contains the possible degrees as integers.

`PossibleChevalleyGroupsFlag`

:

if`G`is not a classical group, this function returns a list of possible almost Chevalley groups modulo scalars. The various Chevalley groups are described by tuples*[ type, rank, p, k ]*, where*type*is a string giving the type (e.g. "2A", see [15, p. 170] for details),*rank*is the rank of the Chevalley group, and*p*is the size of the underlying field.^{k}

`IsPossibleImprimitiveFlag`

:

returns`true`

if`G`might be imprimitive.

`PossibleImprimitiveDimensionsFlag`

:

returns the possible block dimensions (`IsPossibleImprimitiveFlag`

must be`true`

).

`IsPossibleTensorProductFlag`

:

returns`true`

if`G`might be a tensor product.

`PossibleTensorDimensionsFlag`

:

returns the possible tensor product dimensions; note that this entry is only valid if`Is\-Possible\-Tensor\-Product\-Flag`

is`true`

**or**`Is\-Possible\-Tensor\-Power\-Flag`

is true**and**the dimension is a square.

`IsPossibleTensorPowerFlag`

:

returns`true`

if`G`might be a tensor power.

`IsPossibleSmallerFieldFlag`

:

retuns`true`

if`G`could be defined (modulo scalars) over a**smaller**field.

`PossibleSmallerFieldFlag`

:

returns the the least possible field (`IsPossibleSmallerFieldFlag`

must be`true`

).

`IsPossibleSemiLinearFlag`

:

the natural module could be isomorphic to a module of smaller dimension over a**larger**field on which this extension field acts semi-linearly.

`IsPossibleNormalizerPGroupFlag`

:

the dimension of the underlying vector space must be*r*for some prime^{m}*r*and`G`could be an extension of a*r*-group of symplectic type and exponent*r.*gcd*(2,r)*by a subgroup of*Sp(m,r)*, modulo scalars. A*r*-group is of**symplectic type**if every characteristic abelian subgroup is cyclic.

**Examples**

gap> m1 := > [ [ 0*Z(17), Z(17), Z(17)^10, Z(17)^12, Z(17)^2 ], > [ Z(17)^13, Z(17)^10, Z(17)^15, Z(17)^8, Z(17)^0 ], > [ Z(17)^10, Z(17)^6, Z(17)^9, Z(17)^8, Z(17)^10 ], > [ Z(17)^13, Z(17)^5, Z(17)^0, Z(17)^12, Z(17)^5 ], > [ Z(17)^14, Z(17)^13, Z(17)^5, Z(17)^10, Z(17)^0 ] ];; gap> m2 := > [ [ 0*Z(17), Z(17)^10, Z(17)^2, 0*Z(17), Z(17)^10 ], > [ 0*Z(17), Z(17)^6, Z(17)^0, Z(17)^4, Z(17)^15 ], > [ Z(17)^7, Z(17)^6, Z(17)^10, Z(17), Z(17)^2 ], > [ Z(17)^3, Z(17)^10, Z(17)^5, Z(17)^4, Z(17)^6 ], > [ Z(17)^0, Z(17)^8, Z(17)^0, Z(17)^5, Z(17) ] ];; gap> G := Group( m1, m2 );; gap> sl := RecogniseClassicalCLG( G, "all", 1 );; gap> IsSLContainedFlag(sl); "unknown"

Since the algorithm has a random component, it may fail to prove that a
group contains the special linear group even if the group does. As a
reminder, `IsSLContainedFlag`

may return `true`

, `false`

, or
`"unknown"`

.

Here we chose only one random element. If `RecogniseClassicalCLG`

fails
but you suspect that the group contains the special linear group, you can
restart it using more random elements. You should, however, **not** change
the `case`. If you don't already know the case, then call
`RecogniseClassicalCLG`

either without a case parameter or "all".

gap> sl := RecogniseClassicalCLG( G, 5 );; gap> IsSLContainedFlag(sl); true

The following is an example where `G` is not an classical group but
additional information has been obtained.

gap> ReadDataPkg ("matrix", "data", "j1.gap" ); gap> DisplayMat(GeneratorsFlag(G)); 9 1 1 3 1 3 3 1 1 3 1 3 3 9 1 3 1 3 3 9 1 3 1 3 3 9 1 1 1 3 3 9 1 1 3 3 3 9 1 1 3 1 3 9 1 1 3 1 3 . 1 . . . . . . . 1 . . . . . . . 10 . . . . . . . 1 . . . . . . . 10 . . . . . . . 10 10 . . . . . . gap> r := RecogniseClassical( G, "clg" );; gap> ClassicalTypeFlag(r); "unknown" gap> IsPossibleImprimitiveFlag(r); false gap> IsPossibleTensorProductFlag(r); false gap> IsPossibleTensorPowerFlag(r); false gap> PossibleAlmostSimpleFlag(r); [ "J1" ] gap> PossibleAlternatingGroupsFlag(r); [ ] gap> PossibleChevalleyGroupsFlag(r); [ [ "A", 1, 11, 3 ], [ "A", 2, 11, 2 ], [ "A", 3, 11, 1 ], [ "G", 2, 11, 1 ] ]

In this section, we describe functions developed by Niemeyer and Praeger (see [11, 12] for details) to recognise classical groups in their natural representation over finite fields.

`RecogniseClassicalNP( `

`G` [,`case`] [,`N`] )

This is the top-level function taking as input a group `G`, which is a
subgroup of GL*(d,q)* with *d > 2*. The other optional arguments have
the same meaning as those supplied to `RecogniseClassical`

.

The aim of `RecogniseClassicalNP`

is to test whether `G` contains the
subgroup *\O* corresponding to the value of `case`. The algorithm
employed is Monte-Carlo based on random selections of elements from `G`.
`RecogniseClassicalNP`

returns either `true`

or `false`

or ```
"does not
apply"
```

. If it returns `true`

and `G` satisfies the constraints listed
for `case` (see `RecogniseClassical`

) then we know with certainty that
`G` contains the corresponding classical subgroup *Ω*. It is not
checked whether `G` satisfies all these conditions. If it returns
`"does not apply"`

then either the theoretical background of this
algorithm does not allow us to decide whether or not `G` contains
*Ω* (because the parameter values are too small) or `G` is not a
group of type `case`. If it returns `false`

then there is still a
possibility that `G` contains *Ω.* The probability that `G` contains
*Ω* and `RecogniseClassicalNP`

returns `false`

can be controlled by
the parameter *N,* which is the number of elements selected from *G.* The
larger *N* is, the smaller this probability becomes. If *N* is not
passed as an argument, the default value for *N* is 15 if `case` is
`"linear"`

and 25 otherwise. These values were experimentally
determined over a large number of trials. But if *d* has several
distinct prime divisors, larger values of *N* may be required (see [12]).

The complexity of the function for small fields *(q < 2 ^{16})* and for a
given value of

Assume `InfoRecog1`

is set to `Print`

; if `RecogniseClassicalNP`

returns
`true`

, it prints

"Proved that the group contains a classical group of type <case> in <n> selections\",

where `n` is the actual number of elements used; if
`RecogniseClassicalNP`

returns `false`

, it prints ```
"The group probably
does not contain a classical group"
```

and possibly also a statement
suggesting what the group might be.

If `case` is not supplied, then `ClassicalForms`

seeks to determine which
form is preserved. If `ClassicalForms`

fails to find a form, then
`RecogniseClassicalNP`

returns `false`

.

Details of the computation, including the identification of the classical
group type, are stored in the component `G.recognise`

. Its contents can
be accessed using the following flag functions.

`ClassicalTypeFlag`

:

returns one of`"linear"`

,`"symplectic"`

,`"orthogonalplus"`

,`"orthogonalminus"`

,`"orthogonalcircle"`

or`"unitary"`

if`G`is known to be a classical group of this type modulo scalars, otherwise`"unknown"`

.

`IsSLContainedFlag`

:

returns`true`

if`G`contains the special linear group SL*(d,q)*.

`IsSymplecticGroupFlag`

:

returns`true`

if`G`is contained in*\GSp(d,q)*modulo scalars and contains*\Sp(d,q)*.

`IsOrthogonalGroupFlag`

:

returns`true`

if`G`is contained in an orthogonal group modulo scalars and contains the corresponding*Ω*.

`IsUnitaryGroupFlag`

:

returns`true`

if`G`is contained in an unitary group modulo scalars and contains the corresponding*Ω*.

These last four functions return `true`

, `false`

, or "unknown". Both
`true`

and `false`

are **conclusive**. The answer "unknown" indicates
either that the algorithm **failed** to determine whether or not `G` is a
classical group or that the algorithm is not applicable to the supplied
group.

If `RecogniseClassicalNP`

returns `true`

, then `G.recognise`

contains all
the information that proves that *G* contains the classical group having
type `G.recognise.type`

. The record components `d`

, `p`

, `a`

and `q`

identify *G* as a subgroup of *GL(d,q),* where *q= p ^{a}.* For each

`G.recognise.E`

the group `IsPpdElement`

) and for each `G.recognise.LE`

it contains a large
ppd`G.recognise.basic`

. Finally, the component
`G.recognise.isReducible`

is `false`

, indicating that
If `RecogniseClassicalNP`

returns `"does not apply"`

, then *G* has no
record `G.recognise`

.

If `RecogniseClassicalNP`

returns `false`

, then `G.recognise`

gives some
indication as to why the algorithm failed to prove that *G* contains a
classical group. Either *G* could not be shown to be generic and
`G.recognise.isGeneric`

is `false`

and `G.recognise.E`

, `G.recognise.LE`

and `G.recognise.basic`

will indicate which kinds of ppd-elements could
not be found; or `G.recognise.isGeneric`

is `true`

and the algorithm
failed to rule out that *G* preserves an extension field structure and
`G.recognise.possibleOverLargerField`

is `true`

; or `G.isGeneric`

is
`true`

and `G.possibleOverLargerField`

is `false`

and the possibility
that *G* is nearly simple could not be ruled out and
`G.recognise.possibleNearlySimple`

contains a list of names of possible
nearly simple groups; or `ClassicalForms`

failed to find a form and
`G.recognise.noFormFound`

is `true`

; or finally `G.isGeneric`

is `true`

and `G.possibleOverLargerField`

is `false`

and `G.possibleNearlySimple`

is empty and `G` was found to act reducibly and `G.recognise.isReducible`

is `true`

.

If `RecogniseClassicalNP`

returns `false`

, then a recall to
`RecogniseClassicalNP`

for the given group uses the previously computed
facts about the group stored in `G.recognise`

.

gap> RecogniseClassicalNP( GL(10,5), "linear", 10 ); true gap> RecogniseClassicalNP( SP(6,2), "symplectic", 10 ); #I This algorithm does not apply in this case "does not apply"

gap> G := SL(20, 5);; gap> RecogniseClassicalNP( G ); true gap> G.recognise; rec( d := 20, p := 5, a := 1, q := 5, E := [ 11, 12, 16, 18 ], LE := [ 11, 12, 16, 18 ], basic := 12, isReducible := false, isGeneric := true, type := "linear" )

gap> InfoRecog1 := Print;; InfoRecog2 := Print;; gap> G := GeneralUnitaryMatGroup(7,2);; gap> RecogniseClassicalNP( G ); #I The case is unitary #IGacts irreducibly, block criteria failed #I The group is generic in 4 selections #I The group is not an extension field group #I The group does not preserve an extension field #I The group is not nearly simple #I The group acts irreducibly #I Proved that group contains classical group of type unitary #I in 6 random selections. true gap > G.recognise; rec( d := 7, p := 2, a := 2, q := 4, E := [ 5, 7 ], LE := [ 5, 7 ], basic := 7, isReducible := false, isGeneric := true, type := "unitary" )

gap> InfoRecog1 := Print;; InfoRecog2 := Print;; gap> G := Group ( > [[0,1,0,0], > [1,1,0,0], > [0,0,0,1], > [0,0,1,1]] * GF(2).one, > [[0,0,1,0], > [0,1,1,0], > [1,0,1,1], > [0,1,1,1]] * GF(2).one ); gap> RecogniseClassicalNP (G); #I The case is linear #IGacts irreducibly, block criteria failed #I The group is generic in 8 selections #I The group is not an extension field group #I The group does not preserve an extension field #I G' might be A\_7; #I G' is not a Mathieu group; #I G' is not PSL(2,r) #I The group could be a nearly simple group. false gap> G.recognise; rec( d := 4, p := 2, a := 1, q := 2, E := [ 3, 4 ], LE := [ 3 ], basic := 4, isReducible := false, isGeneric := true, possibleNearlySimple := [ "A7" ], dimsReducible := [ 0, 4 ], possibleOverLargerField := false )

We now describe some of the lower-level functions used.

`GenericParameters( `

`G`, `case` )

This function takes as input a subgroup `G` of GL*(d,q)* and a string
`case`, one of the list given under `RecogniseClassicalGroup`

. The group
`G` has generic parameters if the subgroup *Ω* of GL*(d,q)*
contains two different ppd-elements (see `IsPpdElement`

), that is a
ppd*(d, q; e _{1})*-element and a ppd

`GenericParameters`

returns
`true`

if `RecogniseClassicalNP`

can be applied to `false`

.

gap> GenericParameters( SP(6,2), "symplectic" ); false gap> GenericParameters( SP(12,2), "symplectic" ); true

[Comment: In the near future we propose to extend and modify our
algorithm to deal with cases where the group *Ω* does not contain
sufficient ppd-elements.]

`IsGeneric( `

`G`, `N` )

This function takes as input a subgroup `G` of GL*(d,q)* and an integer
`N`. The group `G` is generic if it is irreducible and contains two
different ppd-elements (see `IsPpdElement`

), that is a ppd*(d, q;
e _{1})*-element and a ppd

`G`.recognise.n

by the number of random selections it made. If among
these it finds the two required different ppd-elements, which is
established by examining the sets `G`.recognise.E

, `G`.recognise.LE

,
and `G`.recognise.basic

, then it sets `G`.recognise.isGeneric

to
`true`

and returns `true`

. If after `false`

. It is not
tested whether

gap> IsGeneric( SP(12,2), 20 ); true

`IsExtensionField( `

`G`, `case`, `N` )

This function takes as input a subgroup `G` of GL*(d,q)*, a string
`case`, one of the list given under `RecogniseClassicalGroup`

, and an
integer `N`. It assumes, but does not test that `G` is generic (see
`IsGeneric`

). We say that the group `G` can be defined over an extension
field if there is a prime *b* dividing *d* such that `G` is conjugate to
a subgroup of *Z.*GL*(d/b,q ^{b}).b,* where

`IsExtensionField`

returns `G`.recognise.possibleOverLargerField

is set to `false`

. If, after `IsExtensionField`

returns `true`

. Hence, if it returns `true`

,
then either `G`.recognise.possibleOverLargerField

is set to `true`

.

gap> IsExtensionField( GL(12,2), "linear", 30 ); 8

`IsGenericNearlySimple( `

`G`, `case`, `N` )

The subgroup `G` of GL*(d,q)* is said to be **nearly simple** if there is
some nonabelian simple group *S* such that *S ≤ G/(G∩ Z) ≤
Aut(S),* where

`RecogniseClassicalGroup`

,
and an integer `IsGeneric`

), and is not
contained in an extension field group (see `IsExtensionField`

). This
means that `IsPpdElement`

), that is a ppd`G`.recognise.E

.
At this stage, the theoretical analysis in [12] tells us that either `"linear"`

and `IsGenericNearlySimple`

tries to establish that `"linear"`

, and if so performs further tests.
`IsGenericNearlySimple`

returns `false`

if certain elements are found
which together prove that `G` cannot be a generic nearly simple group.
If, after `N` random selections of elements from `G`, this could not be
shown, then `IsGenericNearlySimple`

returns `true`

and `G` might be a
generic nearly simple group. It increases

by the
number of elements selected. In this case either `G`.recognise.n`G` is nearly simple or
there is a small chance that the output `true`

is incorrect. In fact the
probability with which the algorithm will return the statement `true`

when `G` is not nearly simple can be made arbitrarily small depending on
the number `N` of random selections performed. The list of irreducible
generic nearly simple groups is very short. The name of each nearly
simple group which might be isomorphic to `G` is stored as a string in
`G``.recognise.possibleNearlySimple`

. If `InfoRecog2`

is set to `Print`

,
then in the case that `G` is such a group `IsGeneric`

will print the
isomorphism type of the nearly simple group.

gap> IsGenericNearlySimple( GL(12,2), "symplectic", 30 ); 11

`InducedAction( `

`module`, `basis` )

`SubGModule( `

`module`, `basis` )

`QuotientGModule( `

`module`, `basis` )

These functions take a *G*-module `module` as input, together with a
basis `basis` for a proper submodule, which is assumed to be normalised,
in the weak sense that the first non-zero component of each vector in the
basis is 1, and no two vectors in the basis have their first nonzero
components in the same position. The basis is given as an *r × n*
matrix, where *r* is the length of the basis.

Normally, one runs `IsIrreducible(`

first, and (assuming it
returns `module`)`false`

) then runs these functions using `SubbasisFlag(`

as the second argument. `module`)`InducedAction`

returns a 4-element list
containing the submodule, the quotient module, the original matrices
rewritten with respect to a basis in which a basis for the submodule
comes first, and the change-of-basis matrix; `SubGModule`

returns the
submodule having `basis` as basis; `QuotientGModule`

the corresponding
quotient module.

`RandomIrreducibleSubGModule( `

`module` )

Find a basis for an **irreducible** submodule of `module`.

`FieldGenCentMat( `

`module` )

This function should only be applied if the function
`IsIrreducible(`

has returned `module`)`true`

, and if
`IsAbsolutelyIrreducible(`

has returned `module`)`false`

. A matrix which
centralises the *G*-module `module` and which has multiplicative order
*q ^{e}-1*, where

`CentMatFlag(``module`)

.
`MinimalSubGModules( `

`module1`, `module2` [, `max`] )

This function should only be applied if `IsIrreducible(`

has
returned
`module1`)`true`

. `module1` and `module2` are assumed to be *G*-modules for the
same group *G*. `MinimalSubGModules`

computes and returns a list of the
normalised bases of all of the minimal submodules of `module2` that are
isomorphic to `module1`. (These can then be constructed as *G*-modules,
if required, by calling `SubGModule(`

where `module2`, `basis`)`basis` is
one of these bases.) The optional parameter `max` should be a positive
integer. If the number of submodules exceeds `max`, then the procedure is
aborted.

`SpinBasis( `

`vector`, `matrices` )

The input is a vector, `vector`, and a list of *n × n* matrices,
`matrices`, where *n* is the length of the vector. A normalised basis of
the submodule generated by the action of the matrices (acting on the
right) on the vector is calculated and returned. It is returned as an *r
× n* matrix, where *r* is the dimension of this submodule.

`SpinBasis`

is called by `IsIrreducible`

.

`SemiLinearDecomposition( `

`module`, `S`, `C`, `e` )

`module` is a module for a matrix group *G* over a finite field *GF(q)*.
The function returns `true`

if *G* is found to act semilinearly.

*G* is assumed to act absolutely irreducibly. `S` is a set of invertible
matrices, generating a subgroup of *G*, and assumed to act irreducibly
but not absolutely irreducibly on the underlying vector space of
`module`. The matrix `C` centralises the action of `S` on the underlying
vector space and so acts as multiplication by a field generator *x* of
*GF(q ^{e})* for some embedding of a

`true`

. Otherwise a conjugate of an element of `false`

.
`SemiLinearDecomposition`

is called by `SmashGModule`

.

The algorithm is described in [6].

`TensorProductDecomposition( `

`module`, `basis`, `d1`, `d2` )

`module` is a module for a matrix group *G* over a finite field, `basis`
is a basis of the underlying vector space, `d1` and `d2` are two integers
whose product is the dimension of that space.

`TensorProductDecomposition`

returns `true`

if `module` can be decomposed
as a tensor product of spaces of dimensions `d1` and `d2` with respect to
the given basis, and `false`

otherwise. The matrices which represent the
action of the generators of *G* with respect to the appropriate basis are
computed.

`TensorProductDecomposition`

is called by `SmashGModule`

.

The algorithm is described in [6].

`SymTensorProductDecomposition( `

`module`, `HM` )

`module` is a module for a matrix group *G* over a finite field. `HM` is
the module corresponding to the action of a subgroup *H* of *G* on the
same vector space. Both *G* and *H* are assumed to act absolutely
irreducibly. The function returns `true`

if `HM` can be decomposed as a
tensor product of two or more *H*-modules, all of the same dimension,
where these tensor factors are permuted by the action of *G*. In this
case, components of `module` record the tensor decomposition and the
action of *G* in permuting the factors. If no such decomposition is
found, `SymTensorProductDecomposition`

returns `false`

.

A negative answer is **not** reliable, since we try to find a decomposition
of `HM` as a tensor product only by considering some pseudo-random elements.

`SymTensorProductDecomposition`

is called by `SmashGModule`

.

The algorithm is described in [6].

`ExtraSpecialDecomposition( `

`module`, `S` )

`module` is a module for a matrix group *G* over a finite field where *G*
is assumed to act absolutely irreducibly.

`S` is a set of invertible matrices, assumed to act absolutely
irreducibly on the underlying vector space of `module`.

`ExtraSpecialDecomposition`

returns `true`

if (modulo scalars) *⟨ S
⟩* is an extraspecial *r*-group, for some prime *r*, or a 2-group
of symplectic type (that is, the central product of an extraspecial
2-group with a cyclic group of order 4), normalised by *G*. Otherwise it
returns `false`

.

`ExtraSpecialDecomposition`

attempts to prove that *⟨ S ⟩* is
extraspecial or of symplectic type by construction. It tries to find
generators *x _{1}, ..., x_{k}, y_{1}, ..., y_{k}, z* for

`ExtraSpecialDecomposition`

is called by `SmashGModule`

.

The algorithm is described in [6].

`MinBlocks( `

`module`, `B` )

`MinBlocks`

finds the smallest block containing the echelonised basis `B`
under the action of the *G*-module `module`. The block system record
returned has four components: the number of blocks, a block containing
the supplied basis `B`, a permutation group *P* which describes the
action of *G* on the blocks, and a list which identifies the images of
the generators of `G` as generators of *P*. For an explanation of this
last component, see `ApproximateKernel`

.

`MinBlocks`

is called by `IsPrimitive`

.

The algorithm is described in [7].

`BlockSystemFlag( `

`module` )

If the record for the *G*-module `module` has a block system component,
then `Block\-System\-Flag`

returns this component, which has the
structure described in `Min\-Blocks`

, else it returns `false`

.

The component `.reducible`

is set to `true`

if `module` is known to be
reducible, and to `false`

if it is known not to be. This component is
set by `IsIrreducible`

which may also set the components `.subbasis`

,
`.algEl`

, `.algElMat`

, `.algElCharPol`

, `.algElCharPolFac`

,
`.algElNullspaceVec`

and `.algElNullspaceDim`

. If `module` has been
proved reducible, `.subbasis`

is a basis for a submodule. Alternatively,
if `module` has been proved to be irreducible, `.algEl`

is set to the
random element *el* of the group algebra which has been successfully used
by the algorithm to prove irreducibility, represented abstractly,
essentially as a sum of words in the generators, and `.algElMat`

to the
actual matrix *X* that represents that element. The component
`.algElCharPol`

is set to the characteristic polynomial *p* of *X* and
`.algElCharPolFac`

to the factor *f* of *X* used by the algorithm.
(Essentially irreducibility is proved by applying Norton's
irreducibility criterion to the matrix *f(X)*; see [5] for further
details.) The component `.algElNullspaceVec`

is set to an arbitrary
vector of the nullspace *N* of *f(X)*, and `.algElNullspaceDim`

to the
dimension of *N*.

The component `.absolutelyReducible`

is set to `false`

if `module` is
known to be absolutely irreducible, and to `true`

if it is known not to
be. It is set by `IsAbsolutelyIrreducible`

, which also sets the
components `.degreeFieldExt`

, `.centMat`

, `.centMatMinPoly`

if `module`
is not absolutely irreducible. In that case, `.degreeFieldExt`

is set to
the dimension *e* of the centralising field of `module`. The component
`.centMat`

is set to a matrix *C*, which both centralises each of the
matrices in `module`.generators generating the group action of `module`
and has minimal polynomial *f* of degree *e*. The component
`.centMatMinPoly`

is set equal to *f*.

The component `.semiLinear`

is set to `true`

in `SemiLinearDecomposition`

if *G* acts absolutely irreducibly on `module` but embeds in a group of
semilinear automorphisms over an extension field of degree *e* over the
field *F*. Otherwise it is not set. At the same time, `.degreeFieldExt`

is set to *e*, `.linearPart`

is set to a list of matrices *S* which are
normal subgroup generators for the intersection of *G* with the general
linear group in dimension *d/e* over *GF(q ^{e})*, and

`.centMat`

is set to
a matrix `.frobeniusAutomorphisms`

is
set to a list of integers `.generators`

, such that
The component `.tensorProduct`

is set to `true`

in
`TensorProductDecomposition`

if `module` can be written as a tensor
product of two *G*-modules with respect to an appropriate basis.
Otherwise it is not set. At the same time, `.tensorBasis`

is set to the
appropriate basis of that space, and `.tensorFactors`

to the pair of
*G*-modules whose tensor product is isomorphic to `module` written with
respect to that basis.

The component `.symTensorProduct`

is set to `true`

in
`SymTensorProductDecomposition`

if `module` can be written as a symmetric
tensor product whose factors are permuted by the action of *G*.
Otherwise it is not set. At the same time, `.symTensorBasis`

is set to
the basis with respect to which this decomposition can be found,
`.symTensorFactors`

to the list of tensor factors, and `.symTensorPerm`

to the list of permutations corresponding to the action of each of the
generators of *G* on those tensor factors.

The component `.extraSpecial`

is set to `true`

in the function
`ExtraSpecialDecomposition`

if *G* has been shown to have a normal
subgroup *H* which is an extraspecial *r*-group for an odd prime *r* or a
2-group of symplectic type, modulo scalars. Otherwise it is not set. At
the same time, `.extraSpecialGroup`

is set to the subgroup *H*, and
`.extraSpecialPrime`

is set to *r*.

The component `.imprimitive`

is set to `true`

if *G* has been shown to
act imprimitively and to `false`

if *G* is primitive. Otherwise it is
not set. This component is set in `IsPrimitive`

. If *G* has been shown
to act imprimitively, then `module` has a component `.blockSystem`

which
has the structure described in `BlockSystemFlag`

.

`ApproximateKernel( `

`G`, `P`, `m`, `n` [,`maps`] )

`G` is an irreducible matrix group. `P` is a permutation representation
of `G`.

`ApproximateKernel`

returns a generating set for **a subgroup** of the
kernel of a homomorphism from `G` to `P`. The parameter `m` is the
maximum number of random relations constructed in order to obtain
elements of the kernel. If `n` successive relations provide no **new**
elements of the kernel, then we terminate the construction. These two
parameters determine the time taken to construct the kernel; `n` can be
used to increase the probability that the whole of the kernel is
constructed. The suggested values of `m` and `n` are 100 and 30,
respectively.

Assume that `G` has *r* generators and `P` has *s* generators. The
optional argument `maps` is a list of length *r* containing integers
between *0* and *s*. We use `maps` to specify the correspondence between
the generators of `G` and the generators of `P`. An entry *0* in position
*i* indicates that `G`.i maps to the identity of `P`; an entry *j* in
position *i* indicates that `G`.i maps to `P`.j. By default, we assume
that `G`.i maps to `P`.i.

The function is similar to `RecogniseMatrixGroup`

but here we already
know `.quotient`

is `G` and we have a permutation representation `P` for
`G`. The function returns a record containing information about the
kernel. The record contents can be viewed using `DisplayMatRecord`

.

The algorithm is described in [13]; the implementation is currently
**experimental**.

`RandomRelation( `

`G`, `P` [,`maps`] )

`G` is a matrix group. `P` is a permutation representation of `G`. The
optional argument `maps` has the same meaning as in `ApproximateKernel`

.

`RandomRelation`

returns a relation for `G`. We set up a free group on
the number of generators of `G` and we also set up a mapping from `P` to
this free group. We then take a random word in the free group and
evaluate this in `P`. Our relation is the product of the original word
and the inverse of the image of the permutation under the mapping we have
constructed.

`EvaluateRelation( `

`reln`, `G` )

`reln` is the word returned by an application of `RandomRelation`

.
`EvaluateRelation`

evaluates `reln` on the generators of `G`.

`DisplayMatRecord( `

`rec` [, `layer`] )

`SetPrintLevelFlag( `

`rec`, `i` )

`PrintLevelFlag( `

`rec` )

`rec` is the record returned either by `RecogniseMatrixGroup`

or
`ApproximateKernel`

. The optional argument `layer` is an integer between
1 and the last layer reached by the computation and `i` is an integer
between 1 and 3.

`DisplayMatRecord`

prints the information contained in `rec` according to
three different print level settings. The print level is initially set to
1. This can be changed using `SetPrintLevelFlag`

. We can
also examine the current print level using `PrintLevelFlag`

.

At print level 1, we get basic information about the group; the field
over which it is written, its dimension and possibly its size. If `layer`
is specified, then we get this basic information about `.quotient`

at
that `layer`.

At print level 2, we get the same basic information about the group as we
did at level 1 along with the basic information about `.quotient`

at each
level. If `layer` is specified, then we get the same information as we
did at level 1.

At print level 3, we print the entire contents of `rec`. If `layer` is
specified, then we print the part of `rec` that corresponds to `layer`.

Both `RecogniseMatrixGroup`

and `ApproximateKernel`

return a record whose
components tell us information about the group and the various kernels
which we compute.

Each layer of the record contains basic information about its
corresponding group; the field over which it is written, its identity,
its dimension and its generators. These are stored in components
`.field`

, `.identity`

, `.dimension`

and `.generators`

respectively.

Each layer also has components `.layer\-Number`

, `.type`

, `.size`

and
`.printLevel`

. `.layer\-Number`

is an integer telling us which layer of
the record we are in. The top layer is layer 1, `.kernel`

is layer 2,
etc.

The component `.type`

is one of the following strings: "Unknown",
"Perm", "SL", "Imprimitive", "Trivial" and "PGroup". If `.type`

is "Unknown" then we have not yet computed `.quotient`

. If `.type`

is
"Perm" then we have computed `.quotient`

; if `.quotient`

does not
contain SL then we compute a permutation representation for it. If
`.quotient`

contains SL then `.type`

is "SL". If `.quotient`

is
imprimitive then `.type`

is "Imprimitive". If `.quotient`

is trivial
then `.type`

is "Trivial". If we are in the last layer then `.type`

is
"PGroup".

The component `.size`

is the size of the group generated by
`.generators`

; `.printLevel`

is the current print level (see
`DisplayMatRecord`

).

All layers except the last have components `.sizeQuotient`

,
`.dimQuotient`

, `.basis\-Sub\-module`

and `.basis`

. Here `.sizeQuotient`

is the size of `.quotient`

. If we have a permutation representation for
`.quotient`

which is not faithful, then `.sizeQuotient`

is the size of
the permutation group. We compute the kernel of the action in the next
layer and thus obtain the correct size of `.quotient`

. `.dimQuotient`

is
the dimension of `.quotient`

. The component `.basisSubmodule`

is a
matrix consisting of standard basis vectors for the quotient module. We
use it to check that the `.quotient`

block structure is preserved.
`.basis`

is the basis-change matrix returned when we split the group.

The `.quotient`

record may have `.permDomain`

, `.permGroupP`

, `.fpGroup`

,
`.abstract\-Gen\-erat\-ors`

, `.fpHomomorphism`

and `.isFaithful`

as
components. If we have a permutation representation on the group
`.quotient`

, then `.permDomain`

is either a list of vectors or subspaces
on which the group acts to provide a permutation group. `.permGroupP`

is
the permutation group. `.fpGroup`

is a free group on the number of
generators of `.quotient`

. `.abstractGenerators`

is the generators of
`.fpGroup`

. `.fpHomomorphism`

is a mapping from `.permGroupP`

to
`.fpGroup`

. `.isFaithful`

tells us whether we have learned that the
representation is not faithful.

The `.pGroup`

record has components `.field`

, `.size`

, `.prime`

,
`.dimension`

, `.identity`

, `.layers`

and `.layersVec`

. Here `.field`

is
the field over which the group is written; `.size`

is the size of the
group; `.prime`

is the characteristic of the field; `.dimension`

is the
dimension of the group; `.identity`

is the identity for the group;
`.layers`

and `.layersVec`

are lists of lists of matrices and vectors
respectively which we use to compute the exponents of relations to get
the size of the *p*-group.

`DualGModule( `

`module` )

`module` is a *G*-module. The dual module (obtained by inverting and
transposing the generating matrices) is calculated and returned.

`InducedGModule( `

`G`, `module` )

`G` is a transitive permutation group , and `module` an *H*-module, where
*H* is the subgroup of *G* returned by `Stabilizer(`

. (That
is, the matrix generators for `group`, 1)`module` should correspond to the
permutations generators for *H* returned by this function call.) The
induced *G*-module is calculated and returned. If the action of *H* on
`module` is trivial, then `PermGModule`

should be used instead.

`PermGModule( `

`G`, `field` [, `point`] )

`G` is a permutation group, and `field` a finite field. If `point` is
supplied, it should be an integer in the permutation domain of `G`; by
default, it is 1. The permutation module of `G` on the orbit of `point`
over the field `field` is calculated and returned.

`TensorProductGModule( `

`module1`, `module2` )

The tensor product of the *G*-modules `module1` and `module2` is
calculated and returned.

`WedgeGModule( `

`module` )

The wedge product of the *G*-module `module` -- that is, the action on
anti-symmetric tensors -- is calculated and returned.

`ImprimitiveWreathProduct( `

`G`, `perm-group` )

`G` is a matrix group, a *G*-module, a list of matrices, a permutation
group or a list of permutations, and `perm-group` can be a permutation
group or a list of permutations. Let *G* be the permutation or matrix
group specified or generated by the first argument, *P* the permutation
group specified or generated by the second argument. The wreath product
of *G* and *P* is calculated and returned. This is a matrix group or a
permutation group of dimension or degree *dt*, where *d* is the dimension
or degree of *G* and *t* the degree of *P*. If *G* is a permutation
group, this function has the same effect as `WreathProduct(G, P)`

.

`PowerWreathProduct( `

`G`, `perm-group` )

`G` is a matrix group, a *G*-module, a list of matrices, a permutation
group or a list of permutations, and `perm-group` can be a permutation
group or a list of permutations. Let *G* be the permutation or matrix
group specified or generated by the first argument, and *P* the
permutation group specified or generated by the second argument. The
wreath power of *G* and *P* is calculated and returned. This is a matrix
group or a permutation group of dimension or degree *d ^{t}*, where

`PermGroupRepresentation( `

`G`, `limit` )

`PermGroupRepresentation`

tries to find a permutation representation of
`G` of degree at most `limit`. The function either returns a permutation
group or `false`

if no such representation was found.

Note that `false`

does **not** imply that no such permutation
representation exists. If a permutation representation for `G` is
already known it will be returned **independent** of its degree.

The function tries to find a set of vectors of size at most `limit`
closed under the operation of `G` such that the set spans the whole
vector space; it implements parts of the base-point selection algorithm
described in [10].

gap> m1 := [[0,1],[1,0]] * Z(9);; gap> m2 := [[1,1],[1,0]] * Z(9);; gap> G := Group( m1, m2 );; gap> P := PermGroupRepresentation( G, 100 ); Group( ( 1,15, 4,21, 2,24, 7,30)( 3,18, 5,12, 6,27, 8, 9) (10,16,19,22,14,26,29,32)(11,25,20,31,13,17,28,23), ( 1,15,19,31)( 2,24,29,23)( 3,18,22,11)( 4,21,14,17)( 5,12,26,20) ( 6,27,32,13)( 7,30,10,25)( 8, 9,16,28) ) # note thatlimitis ignored if a representation is known gap> P := PermGroupRepresentation( G, 2 ); Group( ( 1,15, 4,21, 2,24, 7,30)( 3,18, 5,12, 6,27, 8, 9) (10,16,19,22,14,26,29,32)(11,25,20,31,13,17,28,23), ( 1,15,19,31)( 2,24,29,23)( 3,18,22,11)( 4,21,14,17)( 5,12,26,20) ( 6,27,32,13)( 7,30,10,25)( 8, 9,16,28) )

`OrbitMat( `

`G`, `vec`, `base`, `limit` )

`OrbitMat`

computes the orbit of `vec` under the operation of `G`. The
function will return `false`

if this orbit is larger then `limit`.
Otherwise the orbit is return as list of vectors and `base`, which must
be supplied as an empty list, now contains a list of basis vectors
spanning the vector space generated by the orbit.

`GeneralOrthogonalGroup(`

`s`, `d`, `q`)

`O( `

`s`, `d`, `q` )

This function returns the group of isometries fixing a non-degenerate
quadratic form as matrix group. `d` specifies the dimension, `q` the
size of the finite field, and `s` the sign of the quadratic form *Q*. If
the dimension is odd, the sign must be 0. If the dimension is even the
sign must be *-1* or *+1*. The quadratic form *Q* used is returned in
the component `quadraticForm`

, the corresponding symmetric form *β*
is returned in the component `symmetricForm`

.

Given the standard basis *B = {e _{1}, ..., e_{d}}* then

`symmetricForm`

is
the matrix `quadraticForm`

is an upper triangular matrix
There are precisely two isometry classes of geometries in each dimension
`d`. If `d` is even then the geometries are distinguished by the
dimension of the maximal totally singular subspaces. If the sign `s` is
*+1*, then the Witt defect of the underlying vector space is *0*, i.
e. the maximal totally singular subspaces have dimension *<d>/2*; if the
sign is *-1*, the defect is *1*, i.e. the dimension is *<d>/2-1*.

If `d` is odd then the geometries are distinguished by the **discriminant**
of the quadratic form *Q* which is defined as the determinant of
*(f(e _{i},e_{j}))* modulo

`GeneralOrthogonalGroup`

uses a quadratic form
In case of odd dimension, `q` must also be odd because the group ```
O( 0,
2d+1,
```

is isomorphic to the symplectic group *2 ^{k}* )

`Sp( 2d, `*2*^{k} )

and you can use `SP`

to construct it.

gap> G := GeneralOrthogonalGroup(0,5,3); O(0,5,3) gap> Size( G ); 103680 gap> Size( SP(4,3) ); 51840 gap> DeterminantMat(G.1); Z(3)^0 gap> DeterminantMat(G.2); Z(3)

\vbox

gap> DisplayMat( G.symmetricForm ); . 1 . . . 1 . . . . . . 2 . . . . . 2 . . . . . 2

\vbox

gap> DisplayMat( G.quadraticForm ); . 1 . . . . . . . . . . 1 . . . . . 1 . . . . . 1

You can evaluate the quadratic form on a vector by multiplying it from both sides.

gap> v1 := [1,2,0,1,2] * Z(3); [ Z(3), Z(3)^0, 0*Z(3), Z(3), Z(3)^0 ] gap> v1 * G.quadraticForm * v1; Z(3)^0 gap> v1 * G.symmetricForm * v1; Z(3)

`OrderMat(`

`g`)

This function works as described in the **GAP3** manual but uses the
algorithm of [2] for matrices over finite fields.

gap> OrderMat( [ [ Z(17)^4, Z(17)^12, Z(17)^4, Z(17)^7 ], > [ Z(17)^10, Z(17), Z(17)^11, 0*Z(17) ], > [ Z(17)^8, Z(17)^13, Z(17)^0, Z(17)^14 ], > [ Z(17)^14, Z(17)^10, Z(17), Z(17)^10 ] ] ); 5220

`ProjectiveOrderMat(`

`g`)

This function computes the least positive integer *n* such that *g ^{n}* is
scalar; it returns, as a list,

gap> ProjectiveOrderMat( [ [ Z(17)^4, Z(17)^12, Z(17)^4, Z(17)^7 ], > [ Z(17)^10, Z(17), Z(17)^11, 0*Z(17) ], > [ Z(17)^8, Z(17)^13, Z(17)^0, Z(17)^14 ], > [ Z(17)^14, Z(17)^10, Z(17), Z(17)^10 ] ] ); [ 1305, Z(17)^12 ]

`PseudoRandom( `

`G` )

`PseudoRandom( `

`module` )

It takes as input either a matrix group `G`, or a *G*-module `module`
and returns a pseudo-random element. If the supplied record has no seed
stored as a component, then it constructs one (as in `InitPseudoRandom`

).

The algorithm is described in [1].

`InitPseudoRandom( `

`G`, `length`, `n` )

`InitPseudoRandom( `

`module`, `length`, `n` )

`InitPseudoRandom`

takes as input either a matrix group `G`, or a
*G*-module `module`. It constructs a list (or seed) of elements which
can be used to generate pseudo-random elements of the matrix group or
*G*-module. This seed is stored as a component of the supplied record and
can be accessed using `RandomSeedFlag`

.

`InitPseudoRandom`

generates a seed of `length` elements containing
copies of the generators of *G* and performs a total of `n` matrix
multiplications to initialise this list.

The quality of the seed is determined by the value of `n`. For many
applications, `length` = 10 and `n` = 100 seem to suffice; these are the
defaults used by `PseudoRandom`

.

The algorithm is described in [1].

`IsPpdElement( `

`F`, `m`, `d`, `s`, `c`)

For natural numbers *b* and *e* greater than *1* a primitive prime
divisor of *b ^{e} - 1* is a prime dividing

Let *e* be a positive integer greater than *1,* such that *d/2 < e ≤
d.* Let *p* be a prime and *q = p ^{a}.* An element

The function `IsPpdElement`

takes as input a field `F`, and a parameter
`m`, and integers `d`, `s` and `c`, where *s ^{c}* is the size

`IsPpdElement`

returns `false`

.
Otherwise it returns a list of length 2, whose first entry is the integer
`true`

if `false`

if it
is not large. When ~~
~~

~~gap> G := GL (6, 3);; gap> g := [ [ 2, 2, 2, 2, 0, 2 ], > [ 1, 0, 0, 0, 0, 1 ], > [ 2, 2, 1, 0, 0, 0 ], > [ 2, 0, 2, 0, 2, 0 ], > [ 1, 2, 0, 1, 1, 0 ], > [ 1, 2, 2, 1, 2, 0 ] ] * Z(3)^0;; gap> IsPpdElement( G.field, g, 6, 3, 1); [ 5, true ] gap> Collected( Factors( 3^5 - 1) ); [ [ 2, 1 ], [ 11, 2 ] ] gap> Order (G, g) mod 11; 0~~

The algorithm is described in [2] and [11].

`SpinorNorm( `

`form`, `mat` )

computes the spinor norm of `mat` with respect to the symmetric bilinear
`form`.

The underlying field must have odd characteristic.

gap> z := GF(9).root;; gap> m1 := [[0,1,0,0,0,0,0,0,0],[1,2,2,0,0,0,0,0,0], > [0,0,0,1,0,0,0,0,0],[0,0,0,0,1,0,0,0,0],[0,0,0,0,0,1,0,0,0], > [0,0,0,0,0,0,1,0,0],[0,0,0,0,0,0,0,1,0],[0,0,0,0,0,0,0,0,1], > [0,2,1,0,0,0,0,0,0]]*z^0;; gap> m2 := [[z,0,0,0,0,0,0,0,0],[0,z^7,0,0,0,0,0,0,0], > [0,0,1,0,0,0,0,0,0],[0,0,0,1,0,0,0,0,0],[0,0,0,0,1,0,0,0,0], > [0,0,0,0,0,1,0,0,0],[0,0,0,0,0,0,1,0,0],[0,0,0,0,0,0,0,1,0], > [0,0,0,0,0,0,0,0,1]]*z^0;; gap> form := IdentityMat( 9, GF(9) );; gap> form{[1,2]}{[1,2]} := [[0,2],[2,0]] * z^0;; gap> m1 * form * TransposedMat(m1) = form; true gap> m2 * form * TransposedMat(m2) = form; true gap> SpinorNorm( form, m1 ); Z(3)^0 gap> SpinorNorm( form, m2 ); Z(3^2)^5

`Commutators( `

`matrix-list` )

It returns a set containing the **non-trivial** commutators of all pairs of
matrices in `matrix list`.

`IsDiagonal( `

`matrix` )

If a matrix, `matrix`, is diagonal, it returns `true`

, else `false`

.

`IsScalar( `

`matrix` )

If a matrix, `matrix`, is scalar, it returns `true`

, else `false`

.

`DisplayMat( `

`matrix-list` )

`DisplayMat( `

`matrix` )

It converts the entries of a matrix defined over a finite field into
integers and ``pretty-prints" the result. All matrices in `matrix list`
must be defined over the same finite field.

`ChooseRandomElements(`

`G`, `NmrElts`)

`ChooseRandomElements(`

`module`, `NmrElts`)

It takes as input either a matrix group `G`, or a *G*-module `module`,
and returns `NmrElts` pseudo-random elements.

`ElementOfOrder(`

`G`, `RequiredOrder`, `NmrTries`)

`ElementOfOrder(`

`module`, `RequiredOrder`, `NmrTries`)

It takes as input either a matrix group `G`, or a *G*-module `module`,
and searches for an element of order `RequiredOrder`. It examines at
most `NmrTries` elements before returning `false`

.

`ElementWithCharPol(`

`G`, `order`, `pol`, `NmrTries`)

`ElementWithCharPol(`

`module`, `order`, `pol`, `NmrTries`)

It takes as input either a matrix group `G`, or a *G*-module `module`.
It searches for an element of order `order` and characteristic polynomial
`pol`. It examines at most `NmrTries` pseudo-random elements before
returning `false`

.

`LargestPrimeOrderElement(`

`G`, `NmrTries`)

`LargestPrimeOrderElement(`

`module`, `NmrTries`)

It takes as input either a matrix group `G`, or a *G*-module `module`.
It generates `NmrTries` pseudo-random elements and determines which
elements of prime order can be obtained from powers of each; it returns
the largest found and its order as a list.

`LargestPrimePowerOrderElement(`

`G`, `NmrTries`)

`LargestPrimePowerOrderElement(`

`module`, `NmrTries`)

It takes as input either a matrix group `G`, or a *G*-module `module`.
It generates `NmrTries` pseudo-random elements and determines which
elements of prime-power order can be obtained from powers of each; it
returns the largest found and its order as a list.

[1] Frank Celler, Charles R. Leedham-Green, Scott H. Murray, Alice C. Niemeyer, and E.A. O'Brien, ``Generating random elements of a finite group'', Comm. Algebra 23, 4931--4948, 1995.

[2] Frank Celler and C.R. Leedham-Green, ``Calculating the Order of an Invertible Matrix'', ``Groups and Computation II'', Amer. Math. Soc. DIMACS Series 28, 1997.

[3] Frank Celler and C.R. Leedham-Green, ``A Non-Constructive Recognition Algorithm for the Special Linear and Other Classical Groups'', ``Groups and Computation II'', Amer. Math. Soc. DIMACS Series 28, 1997.

[4] Frank Celler and C.R. Leedham-Green, ``A constructive recognition algorithm for the special linear group'', preprint.

[5] Derek F. Holt and Sarah Rees, ``Testing modules for irreducibility'', J. Austral. Math. Soc. Ser. A, 57, 1--16, 1994.

[6] Derek F. Holt, C.R. Leedham-Green, E.A. O'Brien, and Sarah Rees, ``Computing Matrix Group Decompositions with Respect to a Normal Subgroup'', J. Algebra 184, 818--838, 1996.

[7] Derek F. Holt, C.R. Leedham-Green, E.A. O'Brien, and Sarah Rees, ``Testing Matrix Groups for Imprimitivity'', J. Algebra 184, 795--817, 1996.

[8] C.R. Leedham-Green and E.A. O'Brien, ``Tensor Products are Projective Geometries'', to appear J. Algebra.

[9] C.R. Leedham-Green and E.A. O'Brien, ``Recognising tensor products of matrix groups'', to appear Internat. J. Algebra Comput.

[10] Scott H. Murray and E.A. O'Brien, ``Selecting Base Points for the Schreier-Sims Algorithm for Matrix Groups'', J. Symbolic Comput. 19, 577--584, 1995.

[11] Alice C. Niemeyer and Cheryl E. Praeger ``A Recognition Algorithm for Classical Groups over Finite Fields'', submitted to Proceedings of the London Mathematical Society.

[12] Alice C. Niemeyer and Cheryl E. Praeger ``Implementing a Recognition Algorithm for Classical Groups'', ``Groups and Computation II'', Amer. Math. Soc. DIMACS Series 28, 1997.

[13] Anthony Pye, ``Recognising reducible matrix groups'', in preparation.

The following sources provide additional theoretical background to the algorithms.

[14] M. Aschbacher (1984), ``On the maximal subgroups of the finite classical groups'', Invent. Math. 76, 469--514, 1984.

[15] Peter Kleidman and Martin Liebeck, ``The Subgroup Structure of the Finite Classical Groups'', Cambridge University Press, London Math. Soc. Lecture Note Series 129, 1990.

gap3-jm

24 Apr 2021