anchor.diff:  !!
   !! \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 
  +!! To get restriction matrix we need to solve the problem (\e g - vector of Lagrange multipliers in the 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.
  
positive.code: !> \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".
  !!
  !! \section input Input
  !!  - \f$ R_i \f$ - projection matrices to the coarse node supports in the fine space. 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.
  
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]
  