Constructs a parametrized line going from \a p0 to \a p1. */

Constructs a hyperplane passing through the two points. If the dimension of the ambient space * is greater than 2, then there isn't uniqueness, so

Constructs a parametrized line going from \a p0 to \a p1. */

Constructs a hyperplane passing through the two points. If the dimension of the ambient space * is greater than 2, then there isn't uniqueness, so

Default constructor without initialization of the meaningful coefficients. * If Mode==Affine, then the last row is set to [0 ... 0 1] */

Default constructor without initialization of the coefficients. */

Default constructor without initialization. */

Default constructor without initialization. */

\brief Default constructor. * * \param [in] size Positive integer, size of the matrix whose tridiagonal * decomposition will be co

\brief Constructor. * * \param[in] mat The underlying dense matrix */

Default constructor without initialization. */

* \brief Default Constructor. * * The default constructor is useful in cases in which the user intends to * perform decompositions via Bid

Dynamic-size constructor */

* \returns the absolute value of the determinant of the matrix of which * *this is the QR decomposition. * * \warning a determinant

some compilers may fail to optimize std::max etc in case of compile-time constants...

\returns the absolute distance between the plane \c *this and a point \a p. * \sa signedDistance() */

\returns the absolute distance between the plane \c *this and a point \a p. * \sa signedDistance() */

Returns the adjoint transformation */

\sa MatrixBase::adjoint() const */

Creates a stack_memory_handler responsible for the buffer \a ptr of size \a size. * Note that \a ptr can be 0 regardless of the other parameters.

\returns a row (or column) vector expression representing * whether \b all coefficients of each respective column (or row) are \c true. *

\returns A const reference to the vectors containing the alpha values * * This vector permits to reconstruct the j-th eigenvalues as alpha

Compute the LU symbolic factorization of \p matrix using its sparsity pattern. * Several ordering methods can be used at this step. See the PaS

Compute the LL^T symbolic factorization of \p matrix using its sparsity pattern * The result of this operation can be used with successive matri

Compute the LDL^T symbolic factorization of \p matrix using its sparsity pattern * The result of this operation can be used with successive matr

Performs a symbolic decomposition on the sparcity of \a matrix. * * This function is particularly useful when solving for several problems

Performs a symbolic decomposition on the sparcity of \a matrix. * * This function is particularly useful when solving for several problems