\returns the number of rows of the matrix */

* Compute the symbolic and numeric factorization of the input sparse matrix. * The input matrix should be in column-major storage. */

template<typename _Scalar>

\returns the size of the main diagonal, which is min(rows(),cols()). * \sa rows(), cols(), SizeAtCompileTime. */

\returns the distance of a point \a p to its projection onto the line \c *this. * \sa squaredDistance() */

\return the equivalent permutation matrix */

The following is needed for std::complex<Real>:

Given an \a index of a node, on exit, \a outVBegin and \a outVEnd range over the indices of the volume children of the node * and \a outOBegin and

\returns the index of the root of the hierarchy */

\returns the image of the matrix, also called its column-space. The columns of the returned matrix * will form a basis of the image (column-spac

\returns true if the matrix is negative (semidefinite) */

\returns true if the matrix is positive (semidefinite) */

\returns the kernel of the matrix, also called its null-space. The columns of the returned matrix * will form a basis of the kernel. *

\returns an expression of the matrix Q as products of sparse Householder reflectors. * The common usage of this function is to apply it to a dense

\returns the size of the storage major dimension, * i.e., the number of columns for a columns major matrix, and the number of rows otherwise */

\sa MapBase::outerStride() */

\returns an estimate of the reciprocal condition number of the matrix of * which \c *this is the Cholesky decomposition. */

\brief load tensor data from file. returns true on success, false for failure. tokens equal to the number of tensor-length are read, rest are discarde