Goto Chapter: Top 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 Bib Ind
[Top of Book] [Contents] [Previous Chapter] [Next Chapter]

[MathJax on]

10 Chain Complexes
10.1 Chain complex of a simplicial complex and simplicial pair
10.2 Chain complex of a cubical complex and cubical pair
10.3 Chain complex of a regular CW-complex
10.4 Chain Maps of simplicial and regular CW maps
10.5 Constructions for chain complexes
10.6 Filtered chain complexes
10.7 Sparse chain complexes

### 10 Chain Complexes

HAP uses implementations of chain complexes of free K-modules for each of the rings K = Z, K = Q, K = F_p with p a prime number, K = ZG, K = F_pG with G a group. The implemented chain complexes have the form

C_n stackreld_n⟶ C_n-1 stackreld_n-1}⟶ ⋯ stackreld_2⟶ C_1 stackreld_1⟶ C_0 stackreld_0⟶ 0 .

Such a complex is said to have length n and the rank of the free K-module C_k is referred to as the dimenion of the complex in degree k.

For the case K = ZG (resp. K = F_pG) the main focus is on free chain complexes that are exact at each degree k, i.e. im(d_k+1)= ker(d_k), for 0 < k < n and with C_0/ im(d_1) ≅ Z (resp. C_0/ im(d_1) ≅ F_p). We refer to such a chain complex as a resolution of length n even though d_n will typically not be injective. More correct terminology would refer to such a chain complex as the first n degrees of a free resolution.

The following sections illustrate some constructions of chain complexes. Constructions for resolutions are described in the next chapter 11.

#### 10.1 Chain complex of a simplicial complex and simplicial pair

The following example constructs the Quillen simplicial complex Q=mathcal A_p(G) for p=2 and G=A_8; this is the order complex of the poset of non-trivial elementary 2-subgroups of G. The chain complex C_∗ = C_∗(Q) is then computed and seen to have the same number of free generators as Q has simplices. (To ensure indexing of subcomplexes is consistent with that of the large complex it is best to work with vertices represented as integers.)

    gap> Q:=QuillenComplex(AlternatingGroup(8),2);
    Simplicial complex of dimension 3.

    gap> C:=ChainComplex(Q);
    Chain complex of length 3 in characteristic 0 .

    gap> Size(Q);
    55015
    gap> Size(C);
    55015

Next the simplicial complex Q is converted to one whose vertices are represented by integers and a contactible subcomplex L < Q is computed. The chain complex D_∗=C_∗(Q,L) of the simplicial pair (Q,L) is constructed and seen to have the correct size.

    gap> Q:=IntegerSimplicialComplex(Q);
    Simplicial complex of dimension 3.

    gap> L:=ContractibleSubcomplex(Q);
    Simplicial complex of dimension 3.

    gap> D:=ChainComplexOfPair(Q,L);
    Chain complex of length 3 in characteristic 0 .

    gap> Size(D)=Size(Q)-Size(L);
    true
    gap> Size(D);
    670
    gap>

The next commands produce a smalled chain complex B_∗ chain homotopy equivalent to D_∗ and compute the homology H_k(Q, Z) ≅ H_k(B_∗) for k=1,2,3.

    gap> B:=ContractedComplex(D);
    Chain complex of length 3 in characteristic 0 .

    gap> Size(B);
    64
    gap> Homology(B,1);
    [  ]
    gap> Homology(B,2);
    [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
      0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
      0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 ]
    gap> Homology(B,3);
    [  ]

#### 10.2 Chain complex of a cubical complex and cubical pair

The following example reads in the digital image

as a 2-dimensional pure cubical complex M and constructs the chain complex C_∗=C_∗(M).

    gap> K:=ReadImageAsPureCubicalComplex(file,400);
    Pure cubical complex of dimension 2.

    gap> C:=ChainComplex(K);
    Chain complex of length 2 in characteristic 0 .

    gap> Size(C);
    173243

Next an acyclic pure cubical subcomplex L < M is computed and the chain complex D_∗=C_∗(M,L) of the pair is constructed.

    gap> L:=AcyclicSubcomplexOfPureCubicalComplex(K);
    Pure cubical complex of dimension 2.

    gap> D:=ChainComplexOfPair(K,L);
    Chain complex of length 2 in characteristic 0 .

    gap> Size(D);
    618

Finally the chain complex D_∗ is simplified to a homotopy equivalent chain complex B_∗ and the homology H_1(M, Z) ≅ H_1(B_∗) is computed.

    gap> B:=ContractedComplex(D);
    Chain complex of length 2 in characteristic 0 .

    gap> Size(B);
    20
    gap> Homology(B,1);
    [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 ]

#### 10.3 Chain complex of a regular CW-complex

The next example constructs a 15-dimensional regular CW-complex Y that is homotopy equivalent to the 2-dimensional torus.

    gap> Circle:=PureCubicalComplex([[1,1,1,1,1],[1,1,0,1,1],[1,1,1,1,1]]);
    Pure cubical complex of dimension 2.

    gap> Torus:=DirectProductOfPureCubicalComplexes(Circle,Circle);
    Pure cubical complex of dimension 4.

    gap> CTorus:=CechComplexOfPureCubicalComplex(Torus);
    Simplicial complex of dimension 15.

    gap> Y:=RegularCWComplex(CTorus);
    Regular CW-complex of dimension 15

Next the cellular chain complex C_∗=C_∗(Y) is constructed. Also, a minimally generated chain complex D_∗=C_∗(Y') of a non-regular CW-complex Y'≃ Y is constructed.

    gap> C:=ChainComplexOfRegularCWComplex(Y);
    Chain complex of length 15 in characteristic 0 .

    gap> Size(C);
    1172776

    gap> D:=ChainComplex(Y);
    Chain complex of length 15 in characteristic 0 .

    gap> Size(D);
    4

#### 10.4 Chain Maps of simplicial and regular CW maps

The next example realizes the complement of the first prime knot on 11 crossings as a pure permutahedral complex. The complement is converted to a regular CW-complex Y and the boundary inclusion f: ∂ Y ↪ Y is constructed as a map of regular CW-complexes. Then the induced chain map F: C_∗(∂ Y) ↪ C_∗(Y) is constructed. Finally the homology homomorphism H_1(F): H_1(C_∗(∂ Y)) → H_1(C_∗(Y)) is computed.

    gap> K:=PurePermutahedralKnot(11,1);;
    gap> M:=PureComplexComplement(K);
    Pure permutahedral complex of dimension 3.

    gap> Y:=RegularCWComplex(M);
    Regular CW-complex of dimension 3

    gap> f:=BoundaryMap(Y);
    Map of regular CW-complexes

    gap> F:=ChainMap(f);
    Chain Map between complexes of length 2 .

    gap> H:=Homology(F,1);
    [ g1, g2 ] -> [ g1^-1, g1^-1 ]

    gap> Kernel(H);
    Pcp-group with orders [ 0 ]

The command `ChainMap(f)` can be used to construct the chain map C_∗(K) → C_∗(K') induced by a map f: K→ K' of simplicial complexes.

#### 10.5 Constructions for chain complexes

It is straightforward to implement basic constructions on chain complexes. A few constructions are illustrated in the following example.

    gap> res:=ResolutionFiniteGroup(SymmetricGroup(5),5);;
    gap> C:=TensorWithIntegers(res);
    Chain complex of length 5 in characteristic 0 .

    gap> D:=ContractedComplex(C);#A chain homotopic complex
    Chain complex of length 5 in characteristic 0 .
    gap> List([0..5],C!.dimension);
    [ 1, 4, 10, 20, 35, 56 ]
    gap> List([0..5],D!.dimension);
    [ 1, 1, 2, 4, 6, 38 ]

    gap> CxC:=TensorProduct(C,C);
    Chain complex of length 10 in characteristic 0 .

    gap> SC:=SuspendedChainComplex(C);
    Chain complex of length 6 in characteristic 0 .

    gap> RC:=ReducedSuspendedChainComplex(C);
    Chain complex of length 6 in characteristic 0 .

    gap> PC:=PathObjectForChainComplex(C);
    Chain complex of length 5 in characteristic 0 .

    gap> dualC:=HomToIntegers(C);
    Cochain complex of length 5 in characteristic 0 .

    gap> Cxp:=TensorWithIntegersModP(C,5);
    Chain complex of length 5 in characteristic 5 .

    gap> CxQ:=TensorWithRationals(C); #The quirky -1/2 denotes rationals
    Chain complex of length 5 in characteristic -1/2 .

#### 10.6 Filtered chain complexes

A sequence of inclusions of chain complexes C_0,∗ ≤ C_1,∗ ≤ ⋯ ≤ C_T-1,∗ ≤ C_T,∗ in which the preferred basis of C_k-1,ℓ is the beginning of the preferred basis of C_k,ℓ is referred to as a filtered chain complex. Filtered chain complexes give rise to spectral sequences such as the equivariant spectral sequence of a G-CW-complex with subgroup H < G. A particular case is the Lyndon-Hochschild-Serre spectral sequence for the homology of a group extension N ↣ G ↠ Q with E^2_p,q=H_p(Q,H_q(N, Z)).

The following commands construct the filtered chain complex underlying the Lyndon-Hochschild-Serre spectral sequence for the dihedral group G=D_32 of order 64 and its centre N=Z(G).

    gap> G:=DihedralGroup(64);;
    gap> N:=Center(G);;
    gap> R:=ResolutionNormalSeries([G,N],3);;
    gap> C:=FilteredTensorWithIntegersModP(R,2);
    Chain complex of length 3 in characteristic 2 .

The differentials d^r_p,q in a given page E^r of the spectral sequence arise from the induced homology homomorphisms ι^s,t_ℓ: H_ℓ(C_s,∗) → H_ℓ(C_t,∗) for s≤ t. Textbooks traditionally picture the differential in E^r as an array of sloping arrows with non-zero groups E^r_p,q≠ 0 represented by dots. An alternative representation of this information is as a barcode (of the sort used in Topological Data Analysis). The homomorphisms ι^∗,∗_2 in the example, with coefficients converted to mod 2, are pictured by the bar code

which was produced by the following commands.

    gap> p:=2;;k:=2;;
    gap> P:=PersistentHomologyOfFilteredChainComplex(C,k,p);;
    gap> BarCodeDisplay(P);

#### 10.7 Sparse chain complexes

Boundary homomorphisms in all of the above examples of chain complexes are represented by matrices. In cases where the matrices are large and have many zero entries it is better to use sparse matrices.

The following commands demonstrate the conversion of the matrix

A=(beginarrayccc 0 &2 &0 -3 &0 & 0 0 & 0 &4 endarray)

to sparse form, and vice-versa.

    gap> A:=[[0,2,0],[-3,0,0],[0,0,4]];;
    gap> S:=SparseMat(A);
    Sparse matrix with 3 rows and 3 columns in characteristic 0

    gap> NamesOfComponents(S);
    [ "mat", "characteristic", "rows", "cols" ]
    gap> S!.mat;
    [ [ [ 2, 2 ] ], [ [ 1, -3 ] ], [ [ 3, 4 ] ] ]

    gap> B:=SparseMattoMat(S);
    [ [ 0, 2, 0 ], [ -3, 0, 0 ], [ 0, 0, 4 ] ]

To illustrate the use of sparse chain complexes we consider the data points represented in the following digital image.

The following commands read in this image as a 2-dimensional pure cubical complex and store the Euclidean coordinates of the black pixels in a list. Then 200 points are selected at random from this list and used to construct a 200× 200 symmetric matrix S whose entries are the Euclidean distance between the sample data points.

    gap> file:=HapFile("data500.png");;
    gap> M:=ReadImageAsPureCubicalComplex(file,400);;
    gap> A:=M!.binaryArray;;
    gap> data:=[];;
    gap> for i in [1..Length(A)] do
    > for j in [1..Length(A[1])] do
    > if A[i][j]=1 then Add(data,[i,j]); fi;
    > od;
    > od;
    gap> sample:=List([1..200],i->Random(data));;
    gap> S:=VectorsToSymmetricMatrix(sample,EuclideanApproximatedMetric);;

The symmetric distance matrix S is next converted to a filtered chain complex arising from a filtered simplicial complex (using the standard persistent homology pipeline).

    gap> G:=SymmetricMatrixToFilteredGraph(S,10,100);;
    #Filtration length T=10, distances greater than 100 discarded.
    gap> N:=SimplicialNerveOfFilteredGraph(G,2);;
    gap> C:=SparseFilteredChainComplexOfFilteredSimplicialComplex(N);;
    Filtered sparse chain complex of length 2 in characteristic 0 .

Next, the induced homology homomorphisms in degrees 1 and 2, with rational coefficients, are computed and displayed a barcodes.

    gap> P0:=PersistentHomologyOfFilteredSparseChainComplex(C,0);;
    gap> P1:=PersistentHomologyOfFilteredSparseChainComplex(C,1);;
    gap> BarCodeCompactDisplay(P0);

    gap> BarCodeCompactDisplay(P1);

The barcodes are consistent with the data points having been sampled from a space with the homotopy type of an annulus.

[Top of Book] [Contents] [Previous Chapter] [Next Chapter]
Goto Chapter: Top 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 Bib Ind

generated by GAPDoc2HTML