code: public boolean computeShiftInvert(DMatrixRMaj A , double alpha ) {
          initPower(A);
  
          LinearSolverDense solver = LinearSolverFactory_DDRM.linear(A.numCols);
  
          SpecializedOps_DDRM.addIdentity(A,B,-alpha);
          solver.setA(B);
  
          boolean converged = false;
  
          for( int i = 0; i < maxIterations && !converged; i++ ) {
              solver.solve(q0,q1);
              double s = NormOps_DDRM.normPInf(q1);
              CommonOps_DDRM.divide(q1,s,q2);
  
              converged = checkConverged(A);
          }
  
           return converged;
      }
docs: Computes the most dominant eigen vector of A using an inverted shifted matrix.
  The inverted shifted matrix is defined as <b>B = (A - &alpha;I)<sup>-1</sup></b> and
  can converge faster if &alpha; is chosen wisely.
  
  @param A An invertible square matrix matrix.
  @param alpha Shifting factor.
  @return If it converged or not.