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       pcg -- conjugate gradient algorithm.


           template <class Matrix, class Vector, class Preconditioner, class Real>
           int pcg (const Matrix &A, Vector &x, const Vector &b,
             const Preconditioner &M, int &max_iter, Real &tol, odiststream *p_derr=0);


       The simplest call to 'pcg' has the folling form:

           size_t max_iter = 100;
           double tol = 1e-7;
           int status = pcg(a, x, b, EYE, max_iter, tol, &derr);


       pcg solves the symmetric positive definite linear system Ax=b using the Conjugate Gradient

       The return value indicates convergence within  max_iter  (input)  iterations  (0),  or  no
       convergence within max_iter iterations (1).  Upon successful return, output arguments have
       the following values:

       x      approximate solution to Ax = b

              the number of iterations performed before the tolerance was reached

       tol    the residual after the final iteration


       pcg is an iterative template routine.

       pcg follows the algorithm described on p. 15 in

       Templates for the solution of linear systems: building blocks for iterative  methods,  2nd
       Edition, R. Barrett, M. Berry, T. F. Chan, J. Demmel, J. Donato, J. Dongarra, V. Eijkhout,
       R. Pozo, C. Romine, H. Van der Vorst, SIAM, 1994,

       The  present  implementation  is  inspired  from  IML++  1.2  iterative  method   library,


       template <class Matrix, class Vector, class Vector2, class Preconditioner, class Real, class Size>
       int pcg(const Matrix &A, Vector &x, const Vector2 &Mb, const Preconditioner &M,
               Size &max_iter, Real &tol, odiststream *p_derr = 0, std::string label = "cg")
           Vector b = M.solve(Mb);
           Real norm2_b = dot(Mb,b);
           if (norm2_b == Real(0)) norm2_b = 1;
           Vector Mr = Mb - A*x;
           Real  norm2_r = 0;
           if (p_derr) (*p_derr) << "[" << label << "] #iteration residue" << std::endl;
           Vector p;
           for (Size n = 0; n <= max_iter; n++) {
               Vector r = M.solve(Mr);
               Real prev_norm2_r = norm2_r;
               norm2_r = dot(Mr, r);
               if (p_derr) (*p_derr) << "[" << label << "] " << n << " " << sqrt(norm2_r/norm2_b) << std::endl;
               if (norm2_r <= sqr(tol)*norm2_b) {
                 tol = sqrt(norm2_r/norm2_b);
                 max_iter = n;
                 return 0;
               if (n == 0) {
                 p = r;
               } else {
                 Real beta = norm2_r/prev_norm2_r;
                 p = r + beta*p;
               Vector Mq = A*p;
               Real alpha = norm2_r/dot(Mq, p);
               x  += alpha*p;
               Mr -= alpha*Mq;
           tol = sqrt(norm2_r/norm2_b);
           return 1;