anchor.diff:  !> \page p_rcs Robust Coarse Spaces
   !!
  -!! \author Oleg Batrashev, (algorithm by Ivan G. Graham, Robert Scheichl and Jan Van lent)
  -!!
  -!! Implementation is based on the work by Ivan G. Graham, Robert Scheichl and Jan Van lent, "Robust Coarse Spaces for Overlapping Schwarz Methods".
  +!! Implementation is based on the work by Ivan Graham, Robert Scheichl and Jan Van lent, "Robust Coarse Spaces for Overlapping Schwarz Methods".
   !!
   !! \section input Input
  -!!  - \f$ R_i \f$ - projection matrices to the coarse node supports in the fine space. This also identifies aggregates 
  +!!  - \f$ R_i \f$ - injection matrices mapping from the vectors taking 
  +!!       values at all points of the fine mesh to vectors taking values at the 
  +!!       points in the interior of the support of basis function i. This also identifies aggregates 
   !!       with the overlap included (although no aggregate neighbour information is directly available).
   !!  - \e A - system matrix
   !!  - \e <unavailable, ie may leave out for the first implementation> - neighbour information, i.e. which aggregates overlap.
  
positive.code: !!
  !! \subsection general General description
  !!
  !! To get restriction matrix we need to solve the problem (related to energy minimization problem?)
  !!    \f[ B g = 1 \mbox{ where } B = \sum\limits_i^{n_c} =  R_i^T A_i^{-1} R_i 
  !!       \mbox{ and } A_i = R_i A R_i^T \f]
  !! The CG solver (see preconditioner below) can be found in pcg_forRCS(). Because matrix \e B is not sparse special
  !!   datatype RobustCoarseMtx_mod::SumOfInversedSubMtx has been introduced.
  
negative.code: !! Preconditioner \e C for the \e B is
  !! \f[ C = \sum\limits_{j=1}^{n_c} R_j^T B_j^{-1} R_j \f]
  !!
  !! Multiplying a vector by the matrix C can be implemented efficiently 
  !!   using the following formulas which involve only sparse matrices
  !! \f[
  !!   B_j^{-1} = A_j - A_j
  !!     \left[ \begin{array}{ccc} I_{jk} & \cdots & I_{jl} \end{array} \right]
  