Returns minimum element. */

* i n i t */

Returns whether internal memory needs to be de-allocated. * \return BT_TRUE iff internal memory needs to be de-allocated, \n BT_FALSE otherw

\returns a normalized copy of \c *this * \sa normalize(), MatrixBase::normalized() */

* g e t N o r m */

* w r i t e I n t o M a t F i l e */

Preallocates \a reserveSize non zeros */

* r u n O q p B e n c h m a r k */

* \brief Set the initial shift parameter */

* h a s N o L o w e r */

* h a s N o U p p p e r */

* m o v e F r e e T o F i x e d */

* s e t N o L o w e r */

* s e t N o U p p e r */

* s e t u p A l l F r e e */

* c o p y */

* i n i t */

* g e t */

* s e t */

* r e s e t */

* t h r o w W a r n i n g */

* c o p y */

* c o m p u t e C h o l e s k y */

* g e t O b j V a l */

* s e t u p A u x i l i a r y Q P g r a d i e n t */

* s e t u p Q P d a t a */

* b a c k s o l v e T */

* c h o l e s k y D e c o m p o s i t i o n P r o j e c t e d */

* i n i t */

* i s B l o c k i n g */

* s e t A */

* s e t I n f e a s i b i l i t y F l a g */

* s e t u p A u x i l i a r y Q P g r a d i e n t */

\brief Adjoint (conjugate transpose) of the Householder sequence. */

\returns a read-only expression of the Dim x HDim affine part of the transformation */

* a l l o c a t e O u t p u t s */

* a l l o c a t e O u t p u t s */

* a l l o c a t e O u t p u t s */

* Get the number of columns of the input matrix. */

\returns true if \a V (full or thin) is asked for in this SVD decomposition */

Resizes the matrix to \a rows x \a cols while leaving old values untouched. * * The method is intended for matrices of dynamic size. If yo

p [0..n] = cumulative sum of c [0..n-1], and then copy p [0..n-1] into c */

drop entries for which fkeep(A(i,j)) is false; return nz if OK, else -1 */

free a numeric factorization */

x = x + beta * A(:,j), where x is a dense vector and A(:,j) is sparse */

symbolic ordering and analysis for QR or LU */

\returns an expression for the bounding box diagonal vector * if the length of the diagonal is needed: diagonal().norm() * will provide it.