3 The Sparse Matrix Data Type

3.2 Methods and functions for sparse matrices

3.2-1 SparseMatrix

3.2-2 ConvertSparseMatrixToMatrix

3.2-3 CopyMat

3.2-4 GetEntry

3.2-5 SetEntry

3.2-6 AddToEntry

3.2-7 SparseZeroMatrix

3.2-8 SparseIdentityMatrix

3.2-9 TransposedSparseMat

3.2-10 CertainRows

3.2-11 CertainColumns

3.2-12 SparseUnionOfRows

3.2-13 SparseUnionOfColumns

3.2-14 SparseDiagMat

3.2-15 Nrows

3.2-16 Ncols

3.2-17 IndicesOfSparseMatrix

3.2-18 EntriesOfSparseMatrix

3.2-19 RingOfDefinition

3.2-1 SparseMatrix

3.2-2 ConvertSparseMatrixToMatrix

3.2-3 CopyMat

3.2-4 GetEntry

3.2-5 SetEntry

3.2-6 AddToEntry

3.2-7 SparseZeroMatrix

3.2-8 SparseIdentityMatrix

3.2-9 TransposedSparseMat

3.2-10 CertainRows

3.2-11 CertainColumns

3.2-12 SparseUnionOfRows

3.2-13 SparseUnionOfColumns

3.2-14 SparseDiagMat

3.2-15 Nrows

3.2-16 Ncols

3.2-17 IndicesOfSparseMatrix

3.2-18 EntriesOfSparseMatrix

3.2-19 RingOfDefinition

When doing any kind of computation there is a constant conflict between memory load and speed. On the one hand, memory usage is bounded by the total available memory, on the other hand, computation time should also not exceed certain proportions. Memory usage and CPU time are generally inversely proportional, because the computer needs more time to perform operations on a compactified data structure. The idea of sparse matrices mirrors exactly the need for less memory load, therefore it is natural that sparse algorithms take more time than dense ones. However, if the matrix is sufficiently large and sparse at the same time, sparse algorithms can easily be faster than dense ones while maintaining minimal memory load.

It should be noted that, although matrices that appear naturally in homological algebra are almost always sparse, they do not have to stay sparse under (R)REF algorithms, especially when the computation is concerned with transformation matrices. Therefore, in a perfect world there should be ways implemented to not only find out which data structure to use, but also at what point to convert from one to the other. This was, however, not the aim of the **Gauss** package and is just one of many points in which this package could be optimized or extended. Take a look at this matrix \(M\):

0 | 0 | 2 | 9 | 0 |

0 | 5 | 0 | 0 | 0 |

0 | 0 | 0 | 1 | 0 |

The matrix \(M\) carries the same information as the following table, if and only if you know how many rows and columns the matrix has. There is also the matter of the base ring, but this is not important for now:

(i,j) | Entry |

(1,3) | 2 |

(1,4) | 9 |

(2,2) | 5 |

(3,4) | 1 |

This table relates each index tuple to its nonzero entry, all other matrix entries are defined to be zero. This only works for known dimensions of the matrix, otherwise trailing zero rows and columns could get lost (notice how the table gives no hint about the existence of a 5th column). To convert the above table into a sparse data structure, one could list the table entries in this way:

\([ [ 1, 3, 2 ], [ 1, 4, 9 ], [ 2, 2, 5 ], [ 3, 4, 1 ] ]\) |

However, this data structure would not be very efficient. Whenever you are interested in a row \(i\) of \(M\) (this happens all the time when performing Gaussian elimination) the whole list would have to be searched for 3-tuples of the form \([ i, *, * ]\). This is why I tried to manage the row index by putting the tuples into the corresponding list entry:

\([ [ 3, 2 ], [ 4, 9 ] ],\) |

\([ [ 2, 5 ] ],\) |

\([ [ 4, 1 ] ] ]\) |

As you can see, this looks fairly complicated. However, the same information can be stored in this form, which would become the final data structure for **Gauss** sparse matrices:

indices := | [ [ 3, 4 ], | entries:= | [ [ 2, 9 ], |

[ 2 ], | [ 5 ], | ||

[ 4 ] ] | [ 1 ] ] |

Although now the number of rows is equal to the Length of both `indices' and `entries', it is still stored in the sparse matrix. Here is the full data structure (--> `SparseMatrix`

(3.2-1)):

DeclareRepresentation( "IsSparseMatrixRep", IsSparseMatrix, [ "nrows", "ncols", "indices", "entries", "ring" ] );

As you can see, the matrix stores its ring to be on the safe side. This is especially important for zero matrices, as there is no way to determine the base ring from the sparse matrix structure. For further information on sparse matrix construction and converting, refer to `SparseMatrix`

(3.2-1).

DeclareRepresentation( "IsSparseMatrixGF2Rep", IsSparseMatrix, [ "nrows", "ncols", "indices", "ring" ] );

Because the nonzero entries of a matrix over GF(2) are all "1", the entries of M are not stored at all. It is of course crucial that all operations and algorithms make 100% sure that all appearing zero entries are deleted from the `indices' as well as the `entries' list as they arise.

`‣ SparseMatrix` ( mat[, R] ) | ( function ) |

Returns: a sparse matrix over the ring `R`

`‣ SparseMatrix` ( nrows, ncols, indices ) | ( function ) |

Returns: a sparse matrix over GF(2)

`‣ SparseMatrix` ( nrows, ncols, indices, entries[, R] ) | ( function ) |

Returns: a sparse matrix over the ring `R`

The sparse matrix constructor. In the one-argument form the SparseMatrix constructor converts a **GAP** matrix to a sparse matrix. If not provided the base ring `R` is found automatically. For the multi-argument form `nrows` and `ncols` are the dimensions of the matrix. `indices` must be a list of length `nrows` containing lists of the column indices of the matrix in ascending order.

gap> M := [ [ 0 , 1 ], [ 3, 0 ] ] * One( GF(2) ); [ [ 0*Z(2), Z(2)^0 ], [ Z(2)^0, 0*Z(2) ] ] gap> SM := SparseMatrix( M ); <a 2 x 2 sparse matrix over GF(2)> gap> IsSparseMatrix( SM ); true gap> Display( SM ); . 1 1 . gap> SN := SparseMatrix( 2, 2, [ [ 2 ], [ 1 ] ] ); <a 2 x 2 sparse matrix over GF(2)> gap> SN = SM; true gap> SN := SparseMatrix( 2, 3, > [ [ 2 ], [ 1, 3 ] ], > [ [ 1 ], [ 3, 2 ] ] * One( GF(5) ) ); <a 2 x 3 sparse matrix over GF(5)> gap> Display( SN ); . 1 . 3 . 2

`‣ ConvertSparseMatrixToMatrix` ( sm ) | ( method ) |

Returns: a **GAP** matrix, [], or a list of empty lists

This function converts the sparse matrix `sm` into a **GAP** matrix. In case of `nrows(sm)=0`

or `ncols(sm)=0`

the return value is the empty list or a list of empty lists, respectively.

gap> M := [ [ 0 , 1 ], [ 3, 0 ] ] * One( GF(3) ); [ [ 0*Z(3), Z(3)^0 ], [ 0*Z(3), 0*Z(3) ] ] gap> SM := SparseMatrix( M ); <a 2 x 2 sparse matrix over GF(3)> gap> N := ConvertSparseMatrixToMatrix( SM ); [ [ 0*Z(3), Z(3)^0 ], [ 0*Z(3), 0*Z(3) ] ] gap> M = N; true

`‣ CopyMat` ( sm ) | ( method ) |

Returns: a shallow copy of the sparse matrix `sm`

`‣ GetEntry` ( sm, i, j ) | ( method ) |

Returns: a ring element.

This returns the entry `sm[i,j]`

of the sparse matrix `sm`

`‣ SetEntry` ( sm, i, j, elm ) | ( method ) |

Returns: nothing.

This sets the entry `sm[i,j]`

of the sparse matrix `sm` to `elm`.

`‣ AddToEntry` ( sm, i, j, elm ) | ( method ) |

Returns: `true`

or a ring element

AddToEntry adds the element `elm` to the sparse matrix `sm` at the `(i,j)`-th position. This is a Method with a side effect which returns true if you tried to add zero or the sum of `sm[i,j]`

and `elm` otherwise. Please use this method whenever possible.

`‣ SparseZeroMatrix` ( nrows[, ring] ) | ( function ) |

Returns: a sparse <`nrows` x `nrows`> zero matrix over the ring `ring`

`‣ SparseZeroMatrix` ( nrows, ncols[, ring] ) | ( function ) |

Returns: a sparse <`nrows` x `ncols`> zero matrix over the ring `ring`

`‣ SparseIdentityMatrix` ( dim[, ring] ) | ( function ) |

Returns: a sparse <`dim` x `dim`> identity matrix over the ring `ring`. If no ring is specified (one should try to avoid this if possible) the Rationals are the default ring.

`‣ TransposedSparseMat` ( sm ) | ( method ) |

Returns: the transposed matrix of the sparse matrix `sm`

`‣ CertainRows` ( sm, list ) | ( method ) |

Returns: the submatrix `sm{[list]}`

as a sparse matrix

`‣ CertainColumns` ( sm, list ) | ( method ) |

Returns: the submatrix `sm{[1..nrows(sm)]}{[list]}`

as a sparse matrix

`‣ SparseUnionOfRows` ( L ) | ( function ) |

Returns: a sparse matrix

Stack the sparse matrices in the non-empty list `L`.

`‣ SparseUnionOfColumns` ( L ) | ( function ) |

Returns: a sparse matrix

Augment the sparse matrices in the non-empty list `L`.

`‣ SparseDiagMat` ( list ) | ( function ) |

Returns: the block diagonal matrix composed of the sparse matrices in `list`

`‣ Nrows` ( sm ) | ( method ) |

Returns: the number of rows of the sparse matrix `sm`. This should be preferred to the equivalent `sm!.nrows`

.

`‣ Ncols` ( sm ) | ( method ) |

Returns: the number of columns of the sparse matrix `sm`. This should be preferred to the equivalent `sm!.ncols`

.

`‣ IndicesOfSparseMatrix` ( sm ) | ( method ) |

Returns: the indices of the sparse matrix `sm` as a ListList. This should be preferred to the equivalent `sm!.indices`

.

`‣ EntriesOfSparseMatrix` ( sm ) | ( method ) |

Returns: the entries of the sparse matrix `sm` as a ListList of ring elements. This should be preferred to the equivalent `sm!.entries`

and has the additional advantage of working for sparse matrices over GF(2) which do not have any entries.

`‣ RingOfDefinition` ( sm ) | ( method ) |

Returns: the base ring of the sparse matrix `sm`. This should be preferred to the equivalent `sm!.ring`

.

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