anchor.diff:  !! 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]
   !!
  -!! \e B's can be calculated by
  +!! 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]
  
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: !! - precondition_forRCS::initialize() - initializes datatype: creates \e I and \e H matrices
  !! - pcgRobust_mod::precondition_forRCS() applies \e C
  !!
  