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26 Vector and Matrix Objects

26.12 Operations for Row List Matrix Objects

26.12-1 IsPlistVectorRep

26.12-2 IsPlistMatrixRep

26.12-3 List Access for a Row List Matrix

26.12-4 List Assignment for a Row List Matrix

26.12-5 Sublist Access for a Row List Matrix

26.12-6 Sublist Assignment for a Row List Matrix

26.12-9 Add

26.12-10 Remove

26.12-11 Append

26.12-12 ShallowCopy

26.12-13 ListOp

26.12-1 IsPlistVectorRep

26.12-2 IsPlistMatrixRep

26.12-3 List Access for a Row List Matrix

26.12-4 List Assignment for a Row List Matrix

26.12-5 Sublist Access for a Row List Matrix

26.12-6 Sublist Assignment for a Row List Matrix

`26.12-7 IsBound\[\]`

`26.12-8 Unbind\[\]`

26.12-9 Add

26.12-10 Remove

26.12-11 Append

26.12-12 ShallowCopy

26.12-13 ListOp

This chapter describes an interface to vector and matrix objects which are not represented by plain lists (of plain lists), cf. Chapters 23 and 24.

Traditionally, vectors and matrices in **GAP** have been represented by (lists of) lists, see the chapters 23 and 24. More precisely, the term "vector" (corresponding to the filter `IsVector`

(31.14-14)) is used in the abstract sense of an "element of a vector space", the term "row vector" (corresponding to `IsRowVector`

(23.1-1)) is used to denote a "coordinate vector" which is represented by a **GAP** list (see `IsList`

(21.1-1)), and the term "matrix" is used to denote a list of lists, with additional properties (see `IsMatrix`

(24.2-1)).

Unfortunately, such lists (objects in `IsPlistRep`

(21.24-2)) cannot store their type, and so it is impossible to use the advantages of **GAP**'s method selection on them. This situation is unsustainable in the long run since more special representations (compressed, sparse, etc.) have already been and even more will be implemented. Here we describe a programming interface to vectors and matrices, which solves this problem,

The idea of this interface is that **GAP** should be able to represent vectors and matrices by objects that store their type, in order to benefit from method selection. These objects are created by `Objectify`

(79.1-1), we therefore refer to the them as "vector objects" and "matrix objects" respectively.

(Of course the terminology is somewhat confusing: An "abstract matrix" in **GAP** can be represented either by a list of lists or by a matrix object. It can be detected from the filter `IsMatrixOrMatrixObj`

(26.2-3); this is the union of the filters `IsMatrix`

(24.2-1) –which denotes those matrices that are represented by lists of lists– and the filter `IsMatrixObj`

(26.2-2) –which defines "proper" matrix objects in the above sense. In particular, we do *not* regard the objects in `IsMatrix`

(24.2-1) as special cases of objects in `IsMatrixObj`

(26.2-2), or vice versa. Thus one can install specific methods for all three situations: just for "proper" matrix objects, just for matrices represented by lists of lists, or for both kinds of matrices. For example, a **GAP** package may decide to accept only "proper" matrix objects as arguments of its functions, or it may try to support also objects in `IsMatrix`

(24.2-1) as far as this is possible.)

We want to be able to write (efficient) code that is independent of the actual representation (in the sense of **GAP**'s representation filters, see Section 13.4) and preserves it.

This latter requirement makes it necessary to distinguish between different representations of matrices: "Row list" matrices (see `IsRowListMatrix`

(26.2-4) behave basically like lists of rows, in particular the rows are individual **GAP** objects that can be shared between different matrix objects. One can think of other representations of matrices, such as matrices whose subobjects represent columns, or "flat" matrices which do not have subobjects like rows or columns at all. The different kinds of matrices have to be distinguished already with respect to the definition of the operations for them.

In particular vector and matrix objects know their base domain (see `BaseDomain`

(26.3-1)) and their dimensions. The basic condition is that the entries of vector and matrix objects must either lie in the base domain or naturally embed in the sense that addition and multiplication automatically work with elements of the base domain; for example, a matrix object over a polynomial ring may also contain entries from the coefficient ring.

Vector and matrix objects may be mutable or immutable. Of course all operations changing an object are only allowed/implemented for mutable variants.

Vector objects are equal with respect to `\=`

(31.11-1) if they have the same length and the same entries. It is not necessary that they have the same base domain. Matrices are equal with respect to `\=`

(31.11-1) if they have the same dimensions and the same entries.

For a row list matrix object, it is not guaranteed that all its rows have the same vector type. It is for example thinkable that a matrix object stores some of its rows in a sparse representation and some in a dense one. However, it is guaranteed that the rows of two matrices in the same representation are compatible in the sense that all vector operations defined in this interface can be applied to them and that new matrices in the same representation as the original matrix can be formed out of them.

Note that there is neither a default mapping from the set of matrix object representations to the set of vector representations nor one in the reverse direction. There is in general no "associated" vector object representation to a matrix object representation or vice versa. (However, `CompatibleVectorFilter`

(26.3-3) may describe a vector object representation that is compatible with a given matrix object.)

The recommended way to write code that preserves the representation basically works by using constructing operations that take template objects to decide about the intended representation for the new object.

Vector and matrix objects do not have to be **GAP** lists in the sense of `IsList`

(21.1-1). Note that objects not in the filter `IsList`

(21.1-1) need not support all list operations, and their behaviour is not prescribed by the rules for lists, e.g., behaviour w.r.t. arithmetic operations. However, row list matrices behave nearly like lists of row vectors that insist on being dense and containing only vectors of the same length and with the same base domain.

Vector and matrix objects are not likely to benefit from **GAP**'s immediate methods (see section 78.7). Therefore it may be useful to set the filter `IsNoImmediateMethodsObject`

(78.7-2) in the definition of new kinds of vector and matrix objects.

For information on how to implement new `IsMatrixObj`

(26.2-2) and `IsVectorObj`

(26.2-1) representations see Section 26.13.

Currently the following categories of vector and matrix objects are supported in **GAP**. More can be added as soon as there is need for them.

`‣ IsVectorObj` ( obj ) | ( category ) |

The idea behind *vector objects* is that one wants to deal with objects like coefficient lists of fixed length over a given domain R, say, which can be added and can be multiplied from the left with elements from R. A vector object v, say, is always a copyable object (see `IsCopyable`

(12.6-1)) in `IsVector`

(31.14-14), which knows the values of `BaseDomain`

(26.3-1) (with value R) and `Length`

(21.17-5), where R is a domain (see Chapter 12.4) that has methods for `Zero`

(31.10-3), `One`

(31.10-2), `\in`

(30.6-1), `Characteristic`

(31.10-1), `IsFinite`

(30.4-2). We say that v is defined over R. Typically, R will be at least a semiring.

For creating new vector objects compatible with v, the constructor `NewVector`

(26.4-1) requires that also the value of `ConstructingFilter`

(26.3-2) is known for v.

Further, entry access v[i] is expected to return a **GAP** object, for 1 ≤ i ≤` Length`

( v ), and that these entries of v belong to the base domain R.

Note that we do *not* require that v is a list in the sense of `IsList`

(21.1-1), in particular the rules of list arithmetic (see the sections 21.13 and 21.14) need *not* hold. For example, the sum of two vector objects of different lengths or defined over different base domains is not defined, and a plain list of vector objects is not a matrix. Also unbinding entries of vector objects is not defined.

Scalar multiplication from the left is defined only with elements from R.

The family of v (see `FamilyObj`

(13.1-1)) is the same as the family of its base domain R. However, it is *not* required that the entries lie in R in the sense of `\in`

(30.6-1), also values may occur that can be naturally embedded into R. For example, if R is a polynomial ring then some entries in v may be elements of the coefficient ring of R.

`‣ IsMatrixObj` ( obj ) | ( category ) |

The idea behind *matrix objects* is that one wants to deal with objects like m by n arrays over a given domain R, say, which can be added and multiplied and can be multiplied from the left with elements from R. A matrix object M, say, is always a copyable object (see `IsCopyable`

(12.6-1)) in `IsVector`

(31.14-14) and `IsScalar`

(31.14-20), which knows the values of `BaseDomain`

(26.3-1) (with value R), `NumberRows`

(26.3-5) (with value m), `NumberColumns`

(26.3-5) (with value n), where R is a domain (see Chapter 12.4) that has methods for `Zero`

(31.10-3), `One`

(31.10-2), `\in`

(30.6-1), `Characteristic`

(31.10-1), `IsFinite`

(30.4-2). We say that v is defined over R. Typically, R will be at least a semiring.

For creating new matrix objects compatible with M, the constructor `NewMatrix`

(26.4-4) requires that also the value of `ConstructingFilter`

(26.3-2) is known for M.

Further, entry access M[i,j] is expected to return a **GAP** object, for 1 ≤ i ≤ m and 1 ≤ j ≤ n, and that these entries of M belong to the base domain R.

Note that we do *not* require that M is a list in the sense of `IsList`

(21.1-1), in particular the rules of list arithmetic (see the sections 21.13 and 21.14) need *not* hold. For example, accessing "rows" of M via `\[\]`

(21.2-1) is in general not possible, and the sum of two matrix objects with different numbers of rows or columns is not defined. Also unbinding entries of matrix objects is not defined.

Scalar multiplication from the left is defined only with elements from R.

It is not assumed that the multiplication in R is associative, and we do not define what the k-th power of a matrix object is in this case, for positive integers k. (However, a default powering method is available.)

The filter `IsMatrixObj`

alone does *not* imply that the multiplication is the usual matrix multiplication. This multiplication can be defined via the filter `IsOrdinaryMatrix`

(24.2-2); this filter together with the associativity of the base domain also implies the associativity of matrix multiplication. For example, elements of matrix Lie algebras (see `LieObject`

(64.1-1)) lie in `IsMatrixObj`

but not in `IsOrdinaryMatrix`

(24.2-2).

The family of M (see `FamilyObj`

(13.1-1)) is the collections family (see `CollectionsFamily`

(30.2-1)) of its base domain R. However, it is *not* required that the entries lie in R in the sense of `\in`

(30.6-1), also values may occur that can be naturally embedded into R. For example, if R is a polynomial ring then some entries in M may be elements of the coefficient ring of R.

`‣ IsMatrixOrMatrixObj` ( obj ) | ( category ) |

Several functions are defined for objects in `IsMatrix`

(24.2-1) and objects in `IsMatrixObj`

(26.2-2). All these objects lie in the filter `IsMatrixOrMatrixObj`

. It should be used in situations where an object can be either a list of lists in `IsMatrix`

(24.2-1) or a "proper" matrix object in `IsMatrixObj`

(26.2-2), for example as a requirement in the installation of a method for such an argument.

gap> m:= IdentityMat( 2, GF(2) );; gap> IsMatrix( m ); IsMatrixObj( m ); IsMatrixOrMatrixObj( m ); true false true gap> m:= NewIdentityMatrix( IsPlistMatrixRep, GF(2), 2 );; gap> IsMatrix( m ); IsMatrixObj( m ); IsMatrixOrMatrixObj( m ); false true true

`‣ IsRowListMatrix` ( obj ) | ( category ) |

A *row list matrix object* is a matrix object (see `IsMatrixObj`

(26.2-2)) M which admits access to its rows, that is, list access M[i] (see `\[\]`

(21.2-1)) yields the i-th row of M, for 1 ≤ i ≤ `NumberRows( `

M` )`

.

All rows are `IsVectorObj`

(26.2-1) objects in the same representation. Several rows of a row list matrix object can be identical objects, and different row list matrices may share rows. Row access just gives a reference to the row object, without copying the row.

Matrix objects in `IsRowListMatrix`

are *not* necessarily in `IsList`

(21.1-1), and then they need not obey the general rules for lists.

`‣ BaseDomain` ( vector ) | ( attribute ) |

`‣ BaseDomain` ( matrix ) | ( attribute ) |

The vector object `vector` or matrix object `matrix`, respectively, is defined over the domain given by its `BaseDomain`

value.

Note that not all entries of the object necessarily lie in its base domain with respect to `\in`

(30.6-1), see Section 26.1.

`‣ ConstructingFilter` ( v ) | ( attribute ) |

`‣ ConstructingFilter` ( M ) | ( attribute ) |

Returns: a filter

Called with a vector object `v` or a matrix object `M`, respectively, `ConstructingFilter`

returns a filter `f`

such that when `NewVector`

(26.4-1) or `NewMatrix`

(26.4-4), respectively, is called with `f`

then a vector object or a matrix object, respectively, in the same representation as the argument is produced.

`‣ CompatibleVectorFilter` ( M ) | ( attribute ) |

Returns: a filter

Called with a matrix object `M`, `CompatibleVectorFilter`

returns either a filter `f`

such that vector objects with `ConstructingFilter`

(26.3-2) value `f`

are compatible in the sense that `M` can be multiplied with these vector objects, of `fail`

if no such filter is known.

`‣ Length` ( v ) | ( attribute ) |

returns the length of the vector object `v`, which is defined to be the number of entries of `v`.

`‣ NumberRows` ( M ) | ( attribute ) |

`‣ NrRows` ( M ) | ( attribute ) |

`‣ NumberColumns` ( M ) | ( attribute ) |

`‣ NrCols` ( M ) | ( attribute ) |

For a matrix object `M`, `NumberRows`

and `NumberColumns`

store the number of rows and columns of `M`, respectively.

`NrRows`

and `NrCols`

are synonyms of `NumberRows`

and `NumberColumns`

, respectively.

`‣ NewVector` ( filt, R, list ) | ( constructor ) |

`‣ NewZeroVector` ( filt, R, n ) | ( constructor ) |

For a filter `filt`, a semiring `R`, and a list `list` of elements that belong to `R`, `NewVector`

returns a mutable vector object which has the `ConstructingFilter`

(26.3-2) `filt`, the `BaseDomain`

(26.3-1) `R`, and the entries in `list`. The list `list` is guaranteed not to be changed by this operation.

Similarly, `NewZeroVector`

returns a mutable vector object of length `n` which has `filt` and `R` as `ConstructingFilter`

(26.3-2) and `BaseDomain`

(26.3-1) values, and contains the zero of `R` in each position.

`‣ Vector` ( filt, R, list ) | ( operation ) |

`‣ Vector` ( filt, R, vec ) | ( operation ) |

`‣ Vector` ( R, list ) | ( operation ) |

`‣ Vector` ( R, vec ) | ( operation ) |

`‣ Vector` ( list, vec ) | ( operation ) |

`‣ Vector` ( vec1, vec2 ) | ( operation ) |

`‣ Vector` ( list ) | ( operation ) |

Returns: a vector object

If a filter `filt` is given as the first argument then a vector object is returned that has `ConstructingFilter`

(26.3-2) value `filt`, is defined over the base domain `R`, and has the entries given by the list `list` or the vector object `vec`, respectively.

If a semiring `R` is given as the first argument then a vector object is returned whose `ConstructingFilter`

(26.3-2) value is guessed from `R`, again with base domain `R` and entries given by the last argument.

In the remaining cases with two arguments, the first argument is a list or a vector object that defines the entries of the result, and the second argument is a vector object whose `ConstructingFilter`

(26.3-2) and `BaseDomain`

(26.3-1) are taken for the result.

If only a list `list` is given then both the `ConstructingFilter`

(26.3-2) and the `BaseDomain`

(26.3-1) are guessed from this list.

It is *not* guaranteed that the given list of entries is copied.

`‣ ZeroVector` ( filt, R, len ) | ( operation ) |

`‣ ZeroVector` ( R, len ) | ( operation ) |

`‣ ZeroVector` ( len, v ) | ( operation ) |

`‣ ZeroVector` ( len, M ) | ( operation ) |

Returns: a vector object

For a filter `filt`, a semiring `R` and a nonnegative integer `len`, this operation returns a new mutable vector object of length `len` over `R` in the representation `filt` containing only zeros.

If only `R` and `len` are given, then GAP guesses a suitable representation.

For a vector object `v` and a nonnegative integer `len`, this operation returns a new mutable vector object of length `len` in the same representation as `v` containing only zeros.

For a matrix object `M` and a nonnegative integer `len`, this operation returns a new mutable zero vector object of length `len` in the representation given by the `CompatibleVectorFilter`

(26.3-3) value of `M`, provided that such a representation exists.

`‣ NewMatrix` ( filt, R, ncols, list ) | ( constructor ) |

`‣ NewZeroMatrix` ( filt, R, m, n ) | ( constructor ) |

`‣ NewIdentityMatrix` ( filt, R, n ) | ( constructor ) |

For a filter `filt`, a semiring `R`, a positive integer `ncols`, and a list `list`, `NewMatrix`

returns a mutable matrix object which has the `ConstructingFilter`

(26.3-2) `filt`, the `BaseDomain`

(26.3-1) `R`, `n` columns (see `NumberColumns`

(26.3-5)), and the entries described by `list`, which can be either a plain list of vector objects of length `ncols` or a plain list of plain lists of length `ncols` or a plain list of length a multiple of `ncols` containing the entries in row major order. The list `list` is guaranteed not to be changed by this operation.

The corresponding entries must be in or compatible with `R`. If `list` already contains vector objects, they are copied.

Similarly, `NewZeroMatrix`

returns a mutable zero matrix object with `m` rows and `n` columns which has `filt` and `R` as `ConstructingFilter`

(26.3-2) and `BaseDomain`

(26.3-1) values.

Similarly, `NewIdentityMatrix`

returns a mutable identity matrix object with `n` rows and columns which has `filt` and `R` as `ConstructingFilter`

(26.3-2) and `BaseDomain`

(26.3-1) values, and contains the identity element of `R` in the diagonal and the zero of `R` in each off-diagonal position.

`‣ Matrix` ( filt, R, list, ncols ) | ( operation ) |

`‣ Matrix` ( filt, R, list ) | ( operation ) |

`‣ Matrix` ( filt, R, M ) | ( operation ) |

`‣ Matrix` ( R, list, ncols ) | ( operation ) |

`‣ Matrix` ( R, list ) | ( operation ) |

`‣ Matrix` ( R, M ) | ( operation ) |

`‣ Matrix` ( list, ncols, M ) | ( operation ) |

`‣ Matrix` ( list, M ) | ( operation ) |

`‣ Matrix` ( M1, M2 ) | ( operation ) |

`‣ Matrix` ( list, ncols ) | ( operation ) |

`‣ Matrix` ( list ) | ( operation ) |

Returns: a matrix object

If a filter `filt` is given as the first argument then a matrix object is returned that has `ConstructingFilter`

(26.3-2) value `filt`, is defined over the base domain `R`, and has the entries given by the list `list` or the matrix object `M`, respectively. Here `list` can be either a list of plain list that describe the entries of the rows, or a flat list of the entries in row major order, where `ncols` defines the number of columns.

If a semiring `R` is given as the first argument then a matrix object is returned whose `ConstructingFilter`

(26.3-2) value is guessed from `R`, again with base domain `R` and entries given by the last argument.

In those remaining cases where the last argument is a matrix object, the first argument is a list or a matrix object that defines (together with `ncols` if applicable) the entries of the result, and the `ConstructingFilter`

(26.3-2) and `BaseDomain`

(26.3-1) of the last argument are taken for the result.

Finally, if only a list `list` and perhaps `ncols` is given then both the `ConstructingFilter`

(26.3-2) and the `BaseDomain`

(26.3-1) are guessed from the list.

It is guaranteed that the given list `list` is copied in the sense of `ShallowCopy`

(12.7-1). If `list` is a nested list then it is *not* guaranteed that also the entries of `list` are copied.

`‣ ZeroMatrix` ( m, n, M ) | ( operation ) |

`‣ ZeroMatrix` ( R, m, n ) | ( operation ) |

`‣ ZeroMatrix` ( filt, R, m, n ) | ( operation ) |

Returns: a matrix object

For a matrix object `M` and two nonnegative integers `m` and `n`, this operation returns a new fully mutable matrix object with `m` rows and `n` columns in the same representation and over the same base domain as `M` containing only zeros.

If a semiring `R` and two nonnegative integers `m` and `n` are given, the representation of the result is guessed from `R`.

If a filter `filt` and a semiring `R` are given as the first and second argument, they are taken as the values of `ConstructingFilter`

(26.3-2) and `BaseDomain`

(26.3-1) of the result.

`‣ IdentityMatrix` ( n, M ) | ( operation ) |

`‣ IdentityMatrix` ( R, n ) | ( operation ) |

`‣ IdentityMatrix` ( filt, R, n ) | ( operation ) |

Returns: a matrix object

For a matrix object `M` and a nonnegative integer `n`, this operation returns a new fully mutable identity matrix object with `n` rows and columns in the same representation and over the same base domain as `M`.

If a semiring `R` and a nonnegative integer `n` is given, the representation of the result is guessed from `R`.

If a filter `filt` and a semiring `R` are given as the first and second argument, they are taken as the values of `ConstructingFilter`

(26.3-2) and `BaseDomain`

(26.3-1) of the result.

`‣ OneOfBaseDomain` ( v ) | ( attribute ) |

`‣ OneOfBaseDomain` ( M ) | ( attribute ) |

`‣ ZeroOfBaseDomain` ( v ) | ( attribute ) |

`‣ ZeroOfBaseDomain` ( M ) | ( attribute ) |

These attributes return the identity element and the zero element of the `BaseDomain`

(26.3-1) value of the vector object `v` or the matrix object `M`, respectively.

If `v` or `M`, respectively, is a plain list (see `IsPlistRep`

(21.24-2)) then computing its `BaseDomain`

(26.3-1) value can be regarded as expensive, whereas calling `OneOfBaseDomain`

or `ZeroOfBaseDomain`

can be regarded as cheap. If `v` or `M`, respectively, is not a plain list then one can also call `BaseDomain`

(26.3-1) first, without loss of performance.

`‣ \=` ( v1, v2 ) | ( operation ) |

`‣ \=` ( M1, M2 ) | ( operation ) |

`‣ \<` ( v1, v2 ) | ( operation ) |

`‣ \<` ( M1, M2 ) | ( operation ) |

Two vector objects in `IsList`

(21.1-1) are equal if they are equal as lists. Two matrix objects in `IsList`

(21.1-1) are equal if they are equal as lists.

Two vector objects of which at least one is not in `IsList`

(21.1-1) are equal with respect to `\=`

(31.11-1) if they have the same `ConstructingFilter`

(26.3-2) value, the same `BaseDomain`

(26.3-1) value, the same length, and the same entries.

Two matrix objects of which at least one is not in `IsList`

(21.1-1) are equal with respect to `\=`

(31.11-1) if they have the same `ConstructingFilter`

(26.3-2) value, the same `BaseDomain`

(26.3-1) value, the same dimensions, and the same entries.

We do *not* state a general rule how vector and matrix objects shall behave w.r.t. the comparison by `\<`

(31.11-1). Note that a "row lexicographic order" would be quite unnatural for matrices that are internally represented via a list of columns.

Note that the operations `\=`

(31.11-1) and `\<`

(31.11-1) are used to form sorted lists and sets of objects, see for example `Sort`

(21.18-1) and `Set`

(30.3-7).

`‣ Unpack` ( v ) | ( operation ) |

`‣ Unpack` ( M ) | ( operation ) |

Returns: A plain list

Returns a new mutable plain list (see `IsPlistRep`

(21.24-2)) containing the entries of the vector object `v` or the matrix object `M`, respectively. In the case of a matrix object, the result is a plain list of plain lists.

Changing the result does not change `v` or `M`, respectively. The entries themselves are not copied.

`‣ ChangedBaseDomain` ( v, R ) | ( operation ) |

`‣ ChangedBaseDomain` ( M, R ) | ( operation ) |

For a vector object `v` (a matrix object `M`) and a semiring `R`, `ChangedBaseDomain`

returns a new vector object (matrix object) with `BaseDomain`

(26.3-1) value `R`, `ConstructingFilter`

(26.3-2) value equal to that of `v` (`M`), and the same entries as `v` (`M`).

The result is mutable if and only if `v` (`M`) is mutable.

For example, one can create a vector defined over `GF(4)`

from a vector defined over `GF(2)`

with this operation.

`‣ Randomize` ( [Rs, ]v ) | ( operation ) |

`‣ Randomize` ( [Rs, ]M ) | ( operation ) |

Replaces every entry in the mutable vector object `v` or matrix object `M`, respectively, with a random one from the base domain of `v` or `M`, respectively, and returns the argument.

If given, the random source `Rs` is used to compute the random elements. Note that in this case, a `Random`

(14.7-2) method must be available that takes a random source as its first argument and the base domain as its second argument.

The following operations that are defined for lists are useful also for vector objects. (More such operations can be added if this is appropriate.)

`‣ \[\]` ( v, i ) | ( operation ) |

`‣ \[\]\:\=` ( v, i, obj ) | ( operation ) |

`‣ \{\}` ( v, list ) | ( operation ) |

For a vector object `v` and a positive integer `i` that is not larger than the length of `v` (see `Length`

(26.3-4)), `v``[`

`i``]`

is the entry at position `i`.

If `v` is mutable, `i` is as above, and `obj` is an object from the base domain of `v` then `v``[`

`i``]:= `

`obj` assigns `obj` to the `i`-th position of `v`.

If `list` is a list of positive integers that are not larger than the length of `v` then `v``{`

`list``}`

returns a vector object in the same representation as `v` (see `ConstructingFilter`

(26.3-2)) that contains the `list`[ k ]-th entry of `v` at position k.

It is not specified what happens if `i` is larger than the length of `v`, or if `obj` is not in the base domain of `v`, or if `list` contains entries not in the allowed range.

Note that the sublist assignment operation `\{\}\:\=`

(21.4-1) is left out here since it tempts the programmer to use constructions like `v{ [ 1 .. 3 ] }:= w{ [ 4 .. 6 ] }`

which produces an unnecessary intermediate object; one should use `CopySubVector`

(26.9-3) instead.

`‣ PositionNonZero` ( v ) | ( operation ) |

Returns: An integer

Returns the index of the first entry in the vector object `v` that is not zero. If all entries are zero, the function returns `Length(`

.`v`) + 1

`‣ PositionLastNonZero` ( v ) | ( operation ) |

Returns: An integer

Returns the index of the last entry in the vector object `v` that is not zero. If all entries are zero, the function returns 0.

`‣ ListOp` ( v[, func] ) | ( operation ) |

Returns: A plain list

Applies the function `func` to each entry of the vector object `v` and returns the results as a mutable plain list. This allows for calling `List`

(30.3-5) on vector objects.

If the argument `func` is not given, applies `IdFunc`

(5.4-6) to all entries.

`‣ AdditiveInverseMutable` ( v ) | ( operation ) |

`‣ AdditiveInverseSameMutability` ( v ) | ( operation ) |

`‣ ZeroMutable` ( v ) | ( operation ) |

`‣ ZeroSameMutability` ( v ) | ( operation ) |

`‣ IsZero` ( v ) | ( property ) |

`‣ Characteristic` ( v ) | ( attribute ) |

Returns: a vector object

For a vector object `v`, the operations for computing the additive inverse with prescribed mutability return a vector object with the same `ConstructingFilter`

(26.3-2) and `BaseDomain`

(26.3-1) values, such that the sum with `v` is a zero vector. It is not specified what happens if the base domain does not admit the additive inverses of the entries.

Analogously, the operations for computing a zero vector with prescribed mutability return a vector object compatible with `v`.

`IsZero`

returns `true`

if all entries in `v` are zero, and `false`

otherwise.

`Characteristic`

returns the corresponding value of the `BaseDomain`

(26.3-1) value of `v`.

`‣ \+` ( v1, v2 ) | ( method ) |

`‣ \-` ( v1, v2 ) | ( method ) |

`‣ \*` ( s, v ) | ( method ) |

`‣ \*` ( v, s ) | ( method ) |

`‣ \*` ( v1, v2 ) | ( method ) |

`‣ ScalarProduct` ( v1, v2 ) | ( method ) |

`‣ \/` ( v, s ) | ( method ) |

The sum and the difference, respectively, of two vector objects `v1` and `v2` is a new mutable vector object whose entries are the sums and the differences of the entries of the arguments.

The product of a scalar `s` and a vector object `v` (from the left or from the right) is a new mutable vector object whose entries are the corresponding products.

The quotient of a vector object `v` and a scalar `s` is a new mutable vector object whose entries are the corresponding quotients.

The product of two vector objects `v1` and `v2` as well as the result of `ScalarProduct`

is the standard scalar product of the two arguments (an element of the base domain of the vector objects).

All this is defined only if the vector objects have the same length and are defined over the same base domain and have the same representation, and if the products with the given scalar belong to the base domain; otherwise it is not specified what happens. If the result is a vector object then it has the same representation and the same base domain as the given vector object(s).

`‣ AddVector` ( dst, src[, mul[, from, to]] ) | ( operation ) |

`‣ AddVector` ( dst, mul, src[, from, to] ) | ( operation ) |

Returns: nothing

Called with two vector objects `dst` and `src`, this function replaces the entries of `dst` in-place by the entries of the sum `dst`` + `

`src`.

If a scalar `mul` is given as the third or second argument, respectively, then the entries of `dst` get replaced by those of `dst`` + `

`src`` * `

`mul` or `dst`` + `

`mul`` * `

`src`, respectively.

If the optional parameters `from` and `to` are given then only the index range `[`

is guaranteed to be affected. Other indices `from`..`to`]*may* be affected, if it is more convenient to do so. This can be helpful if entries of `src` are known to be zero.

If `from` is bigger than `to`, the operation does nothing.

`‣ MultVector` ( vec, mul[, from, to] ) | ( operation ) |

`‣ MultVectorLeft` ( vec, mul[, from, to] ) | ( operation ) |

`‣ MultVectorRight` ( vec, mul[, from, to] ) | ( operation ) |

Returns: nothing

These operations multiply `vec` by `mul` in-place where `MultVectorLeft`

multiplies with `mul` from the left and `MultVectorRight`

does so from the right.

Note that `MultVector`

is just a synonym for `MultVectorLeft`

. This was chosen because vectors in GAP are by default row vectors and scalar multiplication is usually written as a ⋅ v = a ⋅ [v_1,...,v_n] = [a⋅ v_1,...,a⋅ v_n] with scalars being applied from the left.

If the optional parameters `from` and `to` are given then only the index range `[`

is guaranteed to be affected. Other indices `from`..`to`]*may* be affected, if it is more convenient to do so. This can be helpful if entries of `vec` are known to be zero.

If `from` is bigger than `to`, the operation does nothing.

`‣ ConcatenationOfVectors` ( v1, v2, ... ) | ( function ) |

`‣ ConcatenationOfVectors` ( vlist ) | ( function ) |

Returns: a vector object

Returns a new mutable vector object in the representation of `v1` or the first entry of the nonempty list `vlist` of vector objects, respectively, such that the entries are the concatenation of the given vector objects.

(Note that `Concatenation`

(21.20-1) is a function for which no methods can be installed.)

`‣ ExtractSubVector` ( v, l ) | ( operation ) |

Returns: a vector object

Returns a new mutable vector object of the same vector representation as `v`, containing the entries of `v` at the positions in the list `l`.

This is the same as `v``{`

`l``}`

, the name `ExtractSubVector`

was introduced in analogy to `ExtractSubMatrix`

(26.11-3), for which no equivalent syntax using curly brackets is available.

`‣ CopySubVector` ( src, dst, scols, dcols ) | ( operation ) |

Returns: nothing

For two vector objects `src` and `dst`, such that `dst` is mutable, and two lists `scols` and `dcols` of positions, `CopySubVector`

assigns the entries `src``{ `

`scols`` }`

(see `ExtractSubVector`

(26.9-2)) to the positions `dcols` in `dst`, but without creating an intermediate object and thus –at least in special cases– much more efficiently.

For certain objects like compressed vectors this might be significantly more efficient if `scols` and `dcols` are ranges with increment 1.

`‣ WeightOfVector` ( v ) | ( operation ) |

Returns: an integer

returns the Hamming weight of the vector object `v`, i.e., the number of nonzero entries in `v`.

`‣ DistanceOfVectors` ( v1, v2 ) | ( operation ) |

Returns: an integer

returns the Hamming distance of the vector objects `v1` and `v2`, i.e., the number of entries in which the vectors differ. The vectors must have equal length.

`‣ AdditiveInverseMutable` ( M ) | ( operation ) |

`‣ AdditiveInverseSameMutability` ( M ) | ( operation ) |

`‣ ZeroMutable` ( M ) | ( operation ) |

`‣ ZeroSameMutability` ( M ) | ( operation ) |

`‣ OneMutable` ( M ) | ( operation ) |

`‣ OneSameMutability` ( M ) | ( operation ) |

`‣ InverseMutable` ( M ) | ( operation ) |

`‣ InverseSameMutability` ( M ) | ( operation ) |

`‣ IsZero` ( M ) | ( property ) |

`‣ IsOne` ( M ) | ( property ) |

`‣ Characteristic` ( M ) | ( attribute ) |

Returns: a matrix object

For a vector object `M`, the operations for computing the additive inverse with prescribed mutability return a matrix object with the same `ConstructingFilter`

(26.3-2) and `BaseDomain`

(26.3-1) values, such that the sum with `M` is a zero matrix. It is not specified what happens if the base domain does not admit the additive inverses of the entries.

Analogously, the operations for computing a zero matrix with prescribed mutability return a matrix object compatible with `M`.

The operations for computing an identity matrix with prescribed mutability return a matrix object compatible with `M`, provided that the base domain admits this and `M` is square and nonempty.

Analogously, the operations for computing an inverse matrix with prescribed mutability return a matrix object compatible with `M`, provided that `M` is invertible. (If `M` is not invertible then the operations return `fail`

.)

`IsZero`

returns `true`

if all entries in `M` are zero, and `false`

otherwise. `IsOne`

returns `true`

if `M` is nonempty and square and contains the identity of the base domain in the diagonal, and zero in all other places.

`Characteristic`

returns the corresponding value of the `BaseDomain`

(26.3-1) value of `M`.

`‣ \+` ( M1, M2 ) | ( method ) |

`‣ \-` ( M1, M2 ) | ( method ) |

`‣ \*` ( s, M ) | ( method ) |

`‣ \*` ( M, s ) | ( method ) |

`‣ \*` ( M1, M2 ) | ( method ) |

`‣ \/` ( M, s ) | ( method ) |

`‣ \^` ( M, n ) | ( method ) |

The sum and the difference, respectively, of two matrix objects `M1` and `M2` is a new fully mutable matrix object whose entries are the sums and the differences of the entries of the arguments.

The product of a scalar `s` and a matrix object `M` (from the left or from the right) is a new fully mutable matrix object whose entries are the corresponding products.

The product of two matrix objects `M1` and `M2` is a new fully mutable matrix object; if both `M1` and `M2` are in the filter `IsOrdinaryMatrix`

(24.2-2) then the entries of the result are those of the ordinary matrix product.

The quotient of a matrix object `M` and a scalar `s` is a new fully mutable matrix object whose entries are the corresponding quotients.

For a nonempty square matrix object `M` over an associative base domain, and a positive integer `n`, `M``^`

`n` is a fully mutable matrix object whose entries are those of the `n`-th power of `M`. If `n` is zero then `M``^`

`n` is an identity matrix, and if `n` is a negative integer and `M` is invertible then `M``^`

`n` is the (`-`

`n`)-th power of the inverse of `M`.

All this is defined only if the matrix objects have the same dimensions and are defined over the same base domain and have the same representation, and if the products with the given scalar belong to the base domain; otherwise it is not specified what happens. If the result is a matrix object then it has the same representation and the same base domain as the given matrix object(s).

`‣ MatElm` ( mat, row, col ) | ( operation ) |

Returns: an entry of the matrix object

For a matrix object `mat`, this operation returns the entry in row `row` and column `col`.

Also the syntax `mat``[ `

`row``, `

`col`` ]`

is supported.

Note that this is *not* equivalent to `mat``[ `

`row`` ][ `

`col`` ]`

, which would first try to access `mat``[ `

`row`` ]`

, and this is in general not possible.

`‣ SetMatElm` ( mat, row, col, obj ) | ( operation ) |

Returns: nothing

For a mutable matrix object `mat`, this operation assigns the object `obj` to the position in row `row` and column `col`, provided that `obj` is compatible with the `BaseDomain`

(26.3-1) value of `mat`.

Also the syntax `mat``[ `

`row``, `

`col`` ]:= `

`obj` is supported.

Note that this is *not* equivalent to `mat``[ `

`row`` ][ `

`col`` ]:= `

`obj`, which would first try to access `mat``[ `

`row`` ]`

, and this is in general not possible.

`‣ ExtractSubMatrix` ( mat, rows, cols ) | ( operation ) |

Creates a fully mutable copy of the submatrix described by the two lists, which mean subsets of row and column positions, respectively. This does `mat`{`rows`}{`cols`} and returns the result. It preserves the representation of the matrix.

`‣ MutableCopyMatrix` ( mat ) | ( operation ) |

For a matrix object `mat`, this operation returns a fully mutable copy of `mat`, with the same `ConstructingFilter`

(26.3-2) and `BaseDomain`

(26.3-1) values,

`‣ CopySubMatrix` ( src, dst, srows, drows, scols, dcols ) | ( operation ) |

Returns: nothing

Does

without creating an intermediate object and thus –at least in special cases– much more efficiently. For certain objects like compressed vectors this might be significantly more efficient if `dst`{`drows`}{`dcols`} := `src`{`srows`}{`scols`}`scols` and `dcols` are ranges with increment 1.

`‣ CompatibleVector` ( M ) | ( operation ) |

Returns: a vector object

Called with a matrix object `M` with m rows, this operation returns a zero vector object v of length m and in the representation given by the `CompatibleVectorFilter`

(26.3-3) value of `M` (provided that such a representation exists).

The idea is that there should be an efficient way to form the product v`M`.

`‣ RowsOfMatrix` ( M ) | ( attribute ) |

Returns: a plain list

Called with a matrix object `M`, this operation returns a plain list of objects in the representation given by the `CompatibleVectorFilter`

(26.3-3) value of `M` (provided that such a representation exists), where the i-th entry describes the i-th row of the input.

`‣ CompanionMatrix` ( pol, M ) | ( operation ) |

`‣ CompanionMatrix` ( filt, pol, R ) | ( operation ) |

Returns: a matrix object

For a monic, univariate polynomial `pol` whose coefficients lie in the base domain of the matrix object `M`, `CompanionMatrix`

returns the companion matrix of `pol`, as a matrix object with the same `ConstructingFilter`

(26.3-2) and `BaseDomain`

(26.3-1) values as `M`.

We use row convention, that is, the negatives of the coefficients of `pol` appear in the last row of the result.

If a filter `filt` and a semiring `R` are given then the companion matrix is returned as a matrix object with `ConstructingFilter`

(26.3-2) value `filt` and `BaseDomain`

(26.3-1) value `R`.

In general, matrix objects are not lists in the sense of `IsList`

(21.1-1), and they need not behave like lists, that is, they need not obey all the rules for lists that are stated in Chapter 21. There are situations where one wants to have matrix objects that can on the one hand benefit from **GAP**'s method selection, as is explained in Section 26.1, and do on the other hands support access to **GAP** objects that represent their rows (which are suitable vector objects), such that the operations described in this section are supported for these matrix objects.

One implementation of such matrices is given by the `ConstructingFilter`

(26.3-2) value `IsPlistMatrixRep`

(26.12-2), and any row of these matrices is a vector object in `IsPlistVectorRep`

(26.12-1). Note that these objects do *not* lie in `IsList`

(21.1-1) (and in particular not in `IsPlistRep`

(21.24-2)), thus we are allowed to define the above operations only restrictively, as follows.

Unbinding an entry in a row or unbinding a row in a matrix is allowed only in the last position, that is, the vector and matrix objects insist on being dense. All rows of a matrix must have the same length and the same base domain.

`‣ IsPlistVectorRep` ( obj ) | ( representation ) |

An object `obj` in `IsPlistVectorRep`

describes a vector object (see `IsVectorObj`

(26.2-1)) that can occur as a row in a row list matrix (see Section 26.12). It is internally represented as a positional object (see `IsPositionalObjectRep`

(13.4-1) that stores 2 entries:

its base domain (see

`BaseDomain`

(26.3-1)) anda plain list (see

`IsPlistRep`

(21.24-2) of its entries.

`‣ IsPlistMatrixRep` ( obj ) | ( representation ) |

An object `obj` in `IsPlistMatrixRep`

describes a matrix object (see `IsMatrixObj`

(26.2-2)) that behaves similar to a list of its rows, in the sense defined in Section 26.12. It is internally represented as a positional object (see `IsPositionalObjectRep`

(13.4-1) that stores 4 entries:

its base domain (see

`BaseDomain`

(26.3-1)),an empty vector in the representation of each row,

the number of columns (see

`NumberColumns`

(26.3-5)), anda plain list (see

`IsPlistRep`

(21.24-2) of its rows, each of them being an object in`IsPlistVectorRep`

(26.12-1).

`‣ \[\]` ( mat, pos ) | ( operation ) |

Returns: a vector object

If `mat` is a row list matrix and if `pos` is a positive integer not larger than the number of rows of `mat`, this operation returns the `pos`-th row of `mat`.

It is not specified what happens if `pos` is larger.

`‣ \[\]\:\=` ( mat, pos, vec ) | ( operation ) |

Returns: nothing

If `mat` is a row list matrix, `vec` is a vector object that can occur as a row in `mat` (that is, `vec` has the same base domain, the right length, and the right vector representation), and if `pos` is a positive integer not larger than the number of rows of `mat` plus 1, this operation sets `vec` as the `pos`-th row of `mat`.

In all other situations, it is not specified what happens.

`‣ \{\}` ( mat, poss ) | ( operation ) |

Returns: a row list matrix

For a row list matrix `mat` and a list `poss` of positions, `mat``{ `

`poss`` }`

returns a new mutable row list matrix with the same representation as `mat`, whose rows are identical to the rows at the positions in the list `poss` in `mat`.

`‣ \{\}\:\=` ( mat, poss, mat2 ) | ( operation ) |

Returns: nothing

For a mutable row list matrix `mat`, a list `poss` of positions, and a row list matrix `mat2` of the same vector type and with the same base domain, `mat``{ `

`poss`` }:= `

`mat2` assigns the rows of `mat2` to the positions `poss` in the list of rows of `mat`.

It is not specified what happens if the resulting range of row positions is not dense.

`26.12-7 IsBound\[\]`

`‣ IsBound\[\]` ( mat, pos ) | ( operation ) |

Returns: `true`

or `false`

For a row list matrix `mat` and a positive integer `pos`, `IsBound( `

`mat``[ `

`pos`` ] )`

returns `true`

if `pos` is at most the number of rows of `mat`, and `false`

otherwise.

`26.12-8 Unbind\[\]`

`‣ Unbind\[\]` ( mat, pos ) | ( operation ) |

Returns: nothing

For a mutable row list matrix `mat` with `pos` rows, `Unbind( `

`mat``[ `

`pos`` ] )`

removes the last row. It is not specified what happens if `pos` has another value.

`‣ Add` ( mat, vec[, pos] ) | ( operation ) |

Returns: nothing

For a mutable row list matrix `mat` and a vector object `vec` that is compatible with the rows of `mat`, the two argument version adds `vec` at the end of the list of rows of `mat`.

If a positive integer `pos` is given then `vec` is added in position `pos`, and all later rows are shifted up by one position.

`‣ Remove` ( mat[, pos] ) | ( operation ) |

Returns: a vector object if the removed row exists, otherwise nothing

For a mutable row list matrix `mat`, this operation removes the `pos`-th row and shifts the later rows down by one position. The default for `pos` is the number of rows of `mat`.

If the `pos`-th row existed in `mat` then it is returned, otherwise nothing is returned.

`‣ Append` ( mat1, mat2 ) | ( operation ) |

Returns: nothing

For two row list matrices `mat1`, `mat2` such that `mat1` is mutable and such that the `ConstructingFilter`

(26.3-2) and `BaseDomain`

(26.3-1) values are equal, this operation appends the rows of `mat2` to the rows of `mat1`.

`‣ ShallowCopy` ( mat ) | ( operation ) |

Returns: a matrix object

For a row list matrix `mat`, this operation returns a new mutable matrix with the same `ConstructingFilter`

(26.3-2) and `BaseDomain`

(26.3-1) values as `mat`, which shares its rows with `mat`.

`‣ ListOp` ( mat[, func] ) | ( operation ) |

Returns: a plain list

For a row list matrix `mat`, the variant with one argument returns the plain list (see `IsPlistRep`

(21.24-2)) of its rows, and the variant with two arguments returns the plain list of values of these rows under the function `func`.

Here we list those operations for vector and matrix objects for which no default methods can be installed. When one implements a new type of vector or matrix objects then one has to install specific methods at least for these operations, in order to make the objects behave as described in this chapter. It is advisable to install specific methods also for other operations, for performance reasons. The installations of default methods can be found in the file `lib/matobj.gi`

of the **GAP** distribution. There one can check for which operations it makes sense to overload them for the new type of vector or matrix objects.

*Vector objects*

`BaseDomain`

(26.3-1),`Length`

(26.3-4),`\[\]`

(26.7-1),`\[\]\:\=`

(26.7-1),`ConstructingFilter`

(26.3-2),`NewVector`

(26.4-1).

*Matrix objects*

`BaseDomain`

(26.3-1),`NumberRows`

(26.3-5),`NumberColumns`

(26.3-5),`MatElm`

(26.11-1),`SetMatElm`

(26.11-2),`ConstructingFilter`

(26.3-2),`CompatibleVectorFilter`

(26.3-3),`NewMatrix`

(26.4-4).

`‣ MultMatrixRowLeft` ( mat, i, elm ) | ( operation ) |

`‣ MultMatrixRow` ( mat, i, elm ) | ( operation ) |

Returns: nothing

Multiplies the `i`-th row of the mutable matrix `mat` with the scalar `elm` from the left in-place.

`MultMatrixRow`

is a synonym of `MultMatrixRowLeft`

. This was chosen because linear combinations of rows of matrices are usually written as v ⋅ A = [v_1, ... ,v_n] ⋅ A which multiplies scalars from the left.

`‣ MultMatrixRowRight` ( mat, i, elm ) | ( operation ) |

Returns: nothing

Multiplies the `i`-th row of the mutable matrix `mat` with the scalar `elm` from the right in-place.

`‣ MultMatrixColumnRight` ( mat, i, elm ) | ( operation ) |

`‣ MultMatrixColumn` ( mat, i, elm ) | ( operation ) |

Returns: nothing

Multiplies the `i`-th column of the mutable matrix `mat` with the scalar `elm` from the right in-place.

`MultMatrixColumn`

is a synonym of `MultMatrixColumnRight`

. This was chosen because linear combinations of columns of matrices are usually written as A ⋅ v^T = A ⋅ [v_1, ... ,v_n]^T which multiplies scalars from the right.

`‣ MultMatrixColumnLeft` ( mat, i, elm ) | ( operation ) |

Returns: nothing

Multiplies the `i`-th column of the mutable matrix `mat` with the scalar `elm` from the left in-place.

`‣ AddMatrixRowsLeft` ( mat, i, j, elm ) | ( operation ) |

`‣ AddMatrixRows` ( mat, i, j, elm ) | ( operation ) |

Returns: nothing

Adds the product of `elm` with the `j`-th row of the mutable matrix `mat` to its `i`-th row in-place. The `j`-th row is multiplied with `elm` from the left.

`AddMatrixRows`

is a synonym of `AddMatrixRowsLeft`

. This was chosen because linear combinations of rows of matrices are usually written as v ⋅ A = [v_1, ... ,v_n] ⋅ A which multiplies scalars from the left.

`‣ AddMatrixRowsRight` ( mat, i, j, elm ) | ( operation ) |

Returns: nothing

Adds the product of `elm` with the `j`-th row of the mutable matrix `mat` to its `i`-th row in-place. The `j`-th row is multiplied with `elm` from the right.

`‣ AddMatrixColumnsRight` ( mat, i, j, elm ) | ( operation ) |

`‣ AddMatrixColumns` ( mat, i, j, elm ) | ( operation ) |

Returns: nothing

Adds the product of `elm` with the `j`-th column of the mutable matrix `mat` to its `i`-th column in-place. The `j`-th column is multiplied with `elm` from the right.

`AddMatrixColumns`

is a synonym of `AddMatrixColumnsRight`

. This was chosen because linear combinations of columns of matrices are usually written as A ⋅ v^T = A ⋅ [v_1, ... ,v_n]^T which multiplies scalars from the right.

`‣ AddMatrixColumnsLeft` ( mat, i, j, elm ) | ( operation ) |

Returns: nothing

Adds the product of `elm` with the `j`-th column of the mutable matrix `mat` to its `i`-th column in-place. The `j`-th column is multiplied with `elm` from the left.

`‣ SwapMatrixRows` ( mat, i, j ) | ( operation ) |

Returns: nothing

Swaps the `i`-th row and `j`-th row of a mutable matrix `mat`.

`‣ SwapMatrixColumns` ( mat, i, j ) | ( operation ) |

Returns: nothing

Swaps the `i`-th column and `j`-th column of a mutable matrix `mat`.

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