![]() |
A reference to a dense or sparse row can very conveniently be created via the row()
function. This reference can be treated as any other row vector, i.e. it can be assigned to, it can be copied from, and it can be used in arithmetic operations. The reference can also be used on both sides of an assignment: The row can be either used as an alias to grant write access to a specific row of a matrix primitive on the left-hand side of an assignment or to grant read-access to a specific row of a matrix primitive or expression on the right-hand side of an assignment. The following two examples demonstrate this for dense and sparse matrices:
Similar to the setup of a row, a reference to a dense or sparse column can very conveniently be created via the column()
function. This reference can be treated as any other column vector, i.e. it can be assigned to, copied from, and be used in arithmetic operations. The column can be either used as an alias to grant write access to a specific column of a matrix primitive on the left-hand side of an assignment or to grant read-access to a specific column of a matrix primitive or expression on the right-hand side of an assignment. The following two examples demonstrate this for dense and sparse matrices:
A row view can be used like any other row vector and a column view can be used like any other column vector. For instance, the current number of elements can be obtained via the size()
function, the current capacity via the capacity()
function, and the number of non-zero elements via the nonZeros()
function. However, since rows and columns are references to specific rows and columns of a matrix, several operations are not possible on views, such as resizing and swapping. The following example shows this by means of a row view:
The elements of the row and column can be directly accessed with the subscript operator. The numbering of the row/column elements is
where N is the number of columns/rows of the referenced matrix. Alternatively, the elements of a row or column can be traversed via iterators. Just as with vectors, in case of non-const rows or columns, begin()
and end()
return an Iterator, which allows a manipulation of the non-zero value, in case of a constant rows or columns a ConstIterator is returned:
Especially noteworthy is that row and column views can be created for both row-major and column-major matrices. Whereas the interface of a row-major matrix only allows to traverse a row directly and the interface of a column-major matrix only allows to traverse a column, via views it is possible to traverse a row of a column-major matrix or a column of a row-major matrix. For instance:
However, please note that creating a row view on a matrix stored in a column-major fashion or a column-view on a row-major matrix can result in a considerable performance decrease in comparison to a view on a matrix with a fitting storage orientation. This is due to the non-contiguous storage of the matrix elements. Therefore care has to be taken in the choice of the most suitable storage order:
Although Blaze performs the resulting vector/matrix multiplication as efficiently as possible using a row-major storage order for matrix A would result in a more efficient evaluation.