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Diffstat (limited to 'doc/TutorialSparse.dox')
| -rw-r--r-- | doc/TutorialSparse.dox | 31 |
1 files changed, 29 insertions, 2 deletions
diff --git a/doc/TutorialSparse.dox b/doc/TutorialSparse.dox index fb07adaa2..352907408 100644 --- a/doc/TutorialSparse.dox +++ b/doc/TutorialSparse.dox @@ -241,11 +241,11 @@ In the following \em sm denotes a sparse matrix, \em sv a sparse vector, \em dm sm1.real() sm1.imag() -sm1 0.5*sm1 sm1+sm2 sm1-sm2 sm1.cwiseProduct(sm2) \endcode -However, a strong restriction is that the storage orders must match. For instance, in the following example: +However, <strong>a strong restriction is that the storage orders must match</strong>. For instance, in the following example: \code sm4 = sm1 + sm2 + sm3; \endcode -sm1, sm2, and sm3 must all be row-major or all column major. +sm1, sm2, and sm3 must all be row-major or all column-major. On the other hand, there is no restriction on the target matrix sm4. For instance, this means that for computing \f$ A^T + A \f$, the matrix \f$ A^T \f$ must be evaluated into a temporary matrix of compatible storage order: \code @@ -257,7 +257,14 @@ Binary coefficient wise operators can also mix sparse and dense expressions: \code sm2 = sm1.cwiseProduct(dm1); dm2 = sm1 + dm1; +dm2 = dm1 - sm1; \endcode +Performance-wise, the adding/subtracting sparse and dense matrices is better performed in two steps. For instance, instead of doing <tt>dm2 = sm1 + dm1</tt>, better write: +\code +dm2 = dm1; +dm2 += sm1; +\endcode +This version has the advantage to fully exploit the higher performance of dense storage (no indirection, SIMD, etc.), and to pay the cost of slow sparse evaluation on the few non-zeros of the sparse matrix only. %Sparse expressions also support transposition: @@ -304,6 +311,26 @@ sm2 = sm1.transpose() * P; \endcode +\subsection TutorialSparse_SubMatrices Block operations + +Regarding read-access, sparse matrices expose the same API than for dense matrices to access to sub-matrices such as blocks, columns, and rows. See \ref TutorialBlockOperations for a detailed introduction. +However, for performance reasons, writing to a sub-sparse-matrix is much more limited, and currently only contiguous sets of columns (resp. rows) of a column-major (resp. row-major) SparseMatrix are writable. Moreover, this information has to be known at compile-time, leaving out methods such as <tt>block(...)</tt> and <tt>corner*(...)</tt>. The available API for write-access to a SparseMatrix are summarized below: +\code +SparseMatrix<double,ColMajor> sm1; +sm1.col(j) = ...; +sm1.leftCols(ncols) = ...; +sm1.middleCols(j,ncols) = ...; +sm1.rightCols(ncols) = ...; + +SparseMatrix<double,RowMajor> sm2; +sm2.row(i) = ...; +sm2.topRows(nrows) = ...; +sm2.middleRows(i,nrows) = ...; +sm2.bottomRows(nrows) = ...; +\endcode + +In addition, sparse matrices expose the SparseMatrixBase::innerVector() and SparseMatrixBase::innerVectors() methods, which are aliases to the col/middleCols methods for a column-major storage, and to the row/middleRows methods for a row-major storage. + \subsection TutorialSparse_TriangularSelfadjoint Triangular and selfadjoint views Just as with dense matrices, the triangularView() function can be used to address a triangular part of the matrix, and perform triangular solves with a dense right hand side: |