This chapter explains how to integrate a newly developed group recognition method into the framework provided by the recog package.
TODO: Refer to Chapter 4 for an explanation of methods. There are leaf methods and split methods. The next two sections describe how to implement leaf and split methods respectively, and include example code.
A leaf method must at the very least do the following, examples will be provided below (TODO: add a reference):
Provide the order of the recognized group via SetSize(ri, NNN).
Provide a set of SLPs which map the original generators X to the nice generators Y, as entry for the attribute slptonice.
Provide a function which maps any element g\in G to a corresponding SLP in terms of the nice generators Y, as entry for the attribute slpforelement.
Call SetFilterObj(ri, IsLeaf); to mark the node as a leaf node.
There are further values that can be provided, in particular to speed up computations; we'll come back to that later. Let's first look at an example: The following method is used by recog to recognize trivial groups, as a base case for the recursive group recognition algorithm. It works for arbitrary groups.
TODO: AutoDoc inserts extra paragraph commands here:
BindRecogMethod(FindHomMethodsGeneric, "TrivialGroup",
"go through generators and compare to the identity",
function(ri, G)
local gens;
# get the generators of the group
gens := GeneratorsOfGroup(G);
<P/>
# check whether all generators are trivial
# ri!.isone is explained below
if not ForAll(gens, ri!.isone) then
# NeverApplicable because it makes
# no sense to call this method again
return NeverApplicable;
fi;
<P/>
# The group is trivial! Provide required information:
<P/>
# size of the group
SetSize(ri, 1);
<P/>
# explained below
Setslpforelement(ri, SLPforElementFuncsGeneric.TrivialGroup);
<P/>
# SLP from given generators to nice generators
Setslptonice(ri, StraightLineProgramNC([[[1,0]]],
Length(gens)));
<P/>
# We have reached a leaf node.
SetFilterObj(ri, IsLeaf);
return Success;
end);
The input is in the format described above (TODO), and the return value is "Success".
Two more comments:
When we check whether all generators are the identity, we call ri!.isone, instead of IsOne. The reason for this is the need to support projective groups. For permutation groups and matrix groups, ri!.isone is simply defined to be IsOne. For projective groups, it is set to IsOneProjective, which can be read as "is one modulo scalars".
The function SLPforElementFuncs.TrivialGroup takes ri as well as an element g as input. If g \in G, then it is supposed to return an SLP for g in terms of the nice gens Y. Otherwise it returns fail. Here is the concrete implementation:
SLPforElementFuncsGeneric.TrivialGroup := function(ri,g)
if not ri!.isone(g) then
return fail;
fi;
return StraightLineProgramNC( [ [1,0] ], 1 );
end;
Finally, we need to let recog know about this new recognition method. This is done via the AddMethod function. Another example!
AddMethod(FindHomDbPerm, FindHomMethodsGeneric.TrivialGroup, 300);
TODO: refer to the AddMethod documentation instead. Also this is outdated now. The function AddMethod takes four mandatory arguments db, meth, rank, stamp, and an optional fifth argument comment. Their meaning is as follows:
db is the "method database", and determines to which type of groups the methods should be applied. Allowed values are:
FindHomDbPerm
FindHomDbMatrix
FindHomDbProj
meth is the recognition method we have defined. In our example this is FindHomMethodsGeneric.TrivialGroup.
rank is the relative rank of the recognition method, given as an integer. The idea is that methods with a high rank get called before methods with a low rank, so [recog] tries recognition methods starting from the highest rank. What the "right" rank for a given method is depends on which other methods exist and what their ranks are. As a rule of thumb, methods which are either very fast or very likely to be applicable should be tried before slower methods, or methods which are less likely to be relevant.
stamp holds a string value that uniquely describes the method. This is used for bookkeeping. It is also used in the manual, for printing the recognition tree, and for debugging purposes.
comment is a string valued comment which in the example above has been used to explain what the method does. This argument is optional and can be left out.
Note that above, we only installed our method into FindHomDbPerm. But in recog, it is actually also installed for matrix and projective groups. We reproduce the corresponding AddMethod calls here. Note that the ranks differ, so the same method can be called with varying priority depending on the type of group.
AddMethod(FindHomDbMatrix, FindHomMethodsGeneric.TrivialGroup, 3100);
AddMethod(FindHomDbProjective, FindHomMethodsGeneric.TrivialGroup, 3000);
TODO: more advanced example?
TODO: also explain how verification works
TODO: we need something that demonstrates the other two return values (Oh yes, good point.)
When using the memory of group elements, one currently has to always access ri!.gensHmem instead of doing GroupWithMemory(Grp(ri)). Namely, many functions for objects with memory assume that, if the elements live in the same group, then their !.slp components are identical.
Recall that splitting recognition methods produce an epimorphism \phi:G\to H and then delegate the work to the image H and the kernel N:=\ker(\phi). This means that now N and H have to be constructively recognized. Such a splitting recognition method only needs to provide a homomorphism, by calling SetHomom(ri, hom);. However, in practice one will want to provide additional data.
We start with an example, similar to a method used in recog. This refers to permutation groups only!
BindRecogMethod(FindHomMethodsPerm, "NonTransitive",
"try to find non-transitivity and restrict to orbit",
rec(validatesOrAlwaysValidInput := true),
function(ri, G)
local hom,la,o;
<P/>
# test whether we can do something:
if IsTransitive(G) then
return NeverApplicable;
fi;
<P/>
# compute orbit of the largest moved point
la := LargestMovedPoint(G);
o := Orb(G,la,OnPoints);
Enumerate(o);
# compute homomorphism into Sym(o), i.e, restrict
# the permutation action of G to the orbit o
hom := OrbActionHomomorphism(G,o);
# TODO: explanation
Setvalidatehomominput(ri, {ri,p} -> ForAll(o, x -> (x^p in o)));
# store the homomorphism into the recognition node
SetHomom(ri,hom);
<P/>
# TODO: explanation
Setimmediateverification(ri, true);
<P/>
# indicate success
return Success;
end);
TODO Alternatively use this:
FindHomMethodsPerm.NonTransitive := function(ri, G)
local hom, la, o;
# test whether we can do something:
if IsTransitive(G) then
# the action is transitive, so we can't do
# anything, and there is no point in calling us again.
return NeverApplicable;
fi;
# compute orbit of the largest moved point
la := LargestMovedPoint(G);
o := Orbit(G, la, OnPoints);
# compute homomorphism into Sym(o), i.e, restrict
# the permutation action of G to the orbit o
hom := ActionHomomorphism(G, o);
# store the homomorphism into the recognition node
SetHomom(ri, hom);
# indicate success
return Success;
end;
AddMethod(FindHomDbPerm, FindHomMethodsPerm.NonTransitive, 90);
TODO: More complex example:
FindHomMethodsMatrix.BlockLowerTriangular := function(ri, G)
# This is only used coming from a hint, we know what to do:
# A base change was done to get block lower triangular shape.
# We first do the diagonal blocks, then the lower p-part:
local H, data, hom, newgens;
# we need to construct a homomorphism, but to defined it,
# we need the image, but of course the image is defined in
# terms of the homomorphism... to break this cycle, we do
# the following: we first map the input generators using
# the helper function RECOG.HomOntoBlockDiagonal; this
# function is later also used as the underlying mapping
# of the homomorphism.
data := rec( blocks := ri!.blocks );
newgens := List(GeneratorsOfGroup(G),
x -> RECOG.HomOntoBlockDiagonal(data, x));
Assert(0, not fail in newgens);
# now that we have the images of the generators, we can
# defined the image group
H := Group(newgens);
# finally, we define the homomorphism
hom := GroupHomByFuncWithData(G, H, RECOG.HomOntoBlockDiagonal, data);
# ... and store it in the recognition node
SetHomom(ri, hom);
# since we know exactly what kind of group we are looking
# at, we don't want to run generic recognition on the
# image group and the kernel. So we provide "hints" to
# ensure more appropriate recognition methods are applied
# first.
# Give hint to image
InitialDataForImageRecogNode(ri).blocks := ri!.blocks;
Add(InitialDataForImageRecogNode(ri).hints,
rec( method := FindHomMethodsMatrix.BlockDiagonal,
rank := 2000,
stamp := "BlockDiagonal" ) );
# Tell recog that we have a better method for finding kernel
findgensNmeth(ri).method := FindKernelLowerLeftPGroup;
findgensNmeth(ri).args := [];
# Give hint to kernel N
Add(InitialDataForKernelRecogNode(ri).hints,
rec( method := FindHomMethodsMatrix.LowerLeftPGroup,
rank := 2000,
stamp := “LowerLeftPGroup" ));
InitialDataForKernelRecogNode(ri).blocks := ri!.blocks;
# This function always succeeds, because it is only
# called for inputs for which it is known to apply.
return Success;
end;
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