Lines Matching full:matrices
2 /** \page SparseQuickRefPage Quick reference guide for sparse matrices
17 In this page, we give a quick summary of the main operations available for sparse matrices in the class SparseMatrix. First, it is recommended to read first the introductory tutorial at \ref TutorialSparse. The important point to have in mind when working on sparse matrices is how they are stored :
18 i.e either row major or column major. The default is column major. Most arithmetic operations on sparse matrices will assert that they have the same storage order. Moreover, when interacting with external libraries that are not yet supported by Eigen, it is important to know how to send the required matrix pointers.
21 SparseMatrix is the core class to build and manipulate sparse matrices in Eigen. It takes as template parameters the Scalar type and the storage order, either RowMajor or ColumnMajor. The default is ColumnMajor.
27 The copy constructor and assignment can be used to convert matrices from a storage order to another
89 It is easy to perform arithmetic operations on sparse matrices provided that the dimensions are adequate and that the matrices have the same storage order. Note that the evaluation can always be done in a matrix with a different storage order.
179 \section sparsesubmatrices Sub-matrices
184 sm1.middleRows(start, numRows); // For row major matrices, get a range of numRows rows
185 sm1.middleCols(start, numCols); // For column major matrices, get a range of numCols cols
