## Copyright (C) 2004-2012 Piotr Krzyzanowski
##
## This file is part of Octave.
##
## Octave is free software; you can redistribute it and/or modify it
## under the terms of the GNU General Public License as published by
## the Free Software Foundation; either version 3 of the License, or (at
## your option) any later version.
##
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## WITHOUT ANY WARRANTY; without even the implied warranty of
## MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
## General Public License for more details.
##
## You should have received a copy of the GNU General Public License
## along with Octave; see the file COPYING.  If not, see
## <http://www.gnu.org/licenses/>.

## -*- texinfo -*-
## @deftypefn  {Function File} {@var{x} =} pcg (@var{A}, @var{b}, @var{tol}, @var{maxit}, @var{m1}, @var{m2}, @var{x0}, @dots{})
## @deftypefnx {Function File} {[@var{x}, @var{flag}, @var{relres}, @var{iter}, @var{resvec}, @var{eigest}] =} pcg (@dots{})
##
## Solve the linear system of equations @code{@var{A} * @var{x} = @var{b}}
## by means of the Preconditioned Conjugate Gradient iterative
## method.  The input arguments are
##
## @itemize
## @item
## @var{A} can be either a square (preferably sparse) matrix or a
## function handle, inline function or string containing the name
## of a function which computes @code{@var{A} * @var{x}}.  In principle
## @var{A} should be symmetric and positive definite; if @code{pcg}
## finds @var{A} to not be positive definite, you will get a warning
## message and the @var{flag} output parameter will be set.
##
## @item
## @var{b} is the right hand side vector.
##
## @item
## @var{tol} is the required relative tolerance for the residual error,
## @code{@var{b} - @var{A} * @var{x}}.  The iteration stops if
## @code{norm (@var{b} - @var{A} * @var{x}) <=
##       @var{tol} * norm (@var{b} - @var{A} * @var{x0})}.
## If @var{tol} is empty or is omitted, the function sets
## @code{@var{tol} = 1e-6} by default.
##
## @item
## @var{maxit} is the maximum allowable number of iterations; if
## @code{[]} is supplied for @code{maxit}, or @code{pcg} has less
## arguments, a default value equal to 20 is used.
##
## @item
## @var{m} = @var{m1} * @var{m2} is the (left) preconditioning matrix, so that
## the iteration is (theoretically) equivalent to solving by @code{pcg}
## @code{@var{P} *
## @var{x} = @var{m} \ @var{b}}, with @code{@var{P} = @var{m} \ @var{A}}.
## Note that a proper choice of the preconditioner may dramatically
## improve the overall performance of the method.  Instead of matrices
## @var{m1} and @var{m2}, the user may pass two functions which return
## the results of applying the inverse of @var{m1} and @var{m2} to
## a vector (usually this is the preferred way of using the preconditioner).
## If @code{[]} is supplied for @var{m1}, or @var{m1} is omitted, no
## preconditioning is applied.  If @var{m2} is omitted, @var{m} = @var{m1}
## will be used as preconditioner.
##
## @item
## @var{x0} is the initial guess.  If @var{x0} is empty or omitted, the
## function sets @var{x0} to a zero vector by default.
## @end itemize
##
## The arguments which follow @var{x0} are treated as parameters, and
## passed in a proper way to any of the functions (@var{A} or @var{m})
## which are passed to @code{pcg}.  See the examples below for further
## details.  The output arguments are
##
## @itemize
## @item
## @var{x} is the computed approximation to the solution of
## @code{@var{A} * @var{x} = @var{b}}.
##
## @item
## @var{flag} reports on the convergence.  @code{@var{flag} = 0} means
## the solution converged and the tolerance criterion given by @var{tol}
## is satisfied.  @code{@var{flag} = 1} means that the @var{maxit} limit
## for the iteration count was reached.  @code{@var{flag} = 3} reports that
## the (preconditioned) matrix was found not positive definite.
##
## @item
## @var{relres} is the ratio of the final residual to its initial value,
## measured in the Euclidean norm.
##
## @item
## @var{iter} is the actual number of iterations performed.
##
## @item
## @var{resvec} describes the convergence history of the method.
## @code{@var{resvec} (i,1)} is the Euclidean norm of the residual, and
## @code{@var{resvec} (i,2)} is the preconditioned residual norm,
## after the (@var{i}-1)-th iteration, @code{@var{i} =
## 1, 2, @dots{}, @var{iter}+1}.  The preconditioned residual norm
## is defined as
## @code{norm (@var{r}) ^ 2 = @var{r}' * (@var{m} \ @var{r})} where
## @code{@var{r} = @var{b} - @var{A} * @var{x}}, see also the
## description of @var{m}.  If @var{eigest} is not required, only
## @code{@var{resvec} (:,1)} is returned.
##
## @item
## @var{eigest} returns the estimate for the smallest @code{@var{eigest}
## (1)} and largest @code{@var{eigest} (2)} eigenvalues of the
## preconditioned matrix @code{@var{P} = @var{m} \ @var{A}}.  In
## particular, if no preconditioning is used, the estimates for the
## extreme eigenvalues of @var{A} are returned.  @code{@var{eigest} (1)}
## is an overestimate and @code{@var{eigest} (2)} is an underestimate,
## so that @code{@var{eigest} (2) / @var{eigest} (1)} is a lower bound
## for @code{cond (@var{P}, 2)}, which nevertheless in the limit should
## theoretically be equal to the actual value of the condition number.
## The method which computes @var{eigest} works only for symmetric positive
## definite @var{A} and @var{m}, and the user is responsible for
## verifying this assumption.
## @end itemize
##
## Let us consider a trivial problem with a diagonal matrix (we exploit the
## sparsity of A)
##
## @example
## @group
##      n = 10;
##      A = diag (sparse (1:n));
##      b = rand (n, 1);
##      [l, u, p, q] = luinc (A, 1.e-3);
## @end group
## @end example
##
## @sc{Example 1:} Simplest use of @code{pcg}
##
## @example
##   x = pcg(A,b)
## @end example
##
## @sc{Example 2:} @code{pcg} with a function which computes
## @code{@var{A} * @var{x}}
##
## @example
## @group
##   function y = apply_a (x)
##     y = [1:N]'.*x;
##   endfunction
##
##   x = pcg ("apply_a", b)
## @end group
## @end example
##
## @sc{Example 3:} @code{pcg} with a preconditioner: @var{l} * @var{u}
##
## @example
## x = pcg (A, b, 1.e-6, 500, l*u);
## @end example
##
## @sc{Example 4:} @code{pcg} with a preconditioner: @var{l} * @var{u}.
## Faster than @sc{Example 3} since lower and upper triangular matrices
## are easier to invert
##
## @example
## x = pcg (A, b, 1.e-6, 500, l, u);
## @end example
##
## @sc{Example 5:} Preconditioned iteration, with full diagnostics.  The
## preconditioner (quite strange, because even the original matrix
## @var{A} is trivial) is defined as a function
##
## @example
## @group
##   function y = apply_m (x)
##     k = floor (length (x) - 2);
##     y = x;
##     y(1:k) = x(1:k)./[1:k]';
##   endfunction
##
##   [x, flag, relres, iter, resvec, eigest] = ...
##                      pcg (A, b, [], [], "apply_m");
##   semilogy (1:iter+1, resvec);
## @end group
## @end example
##
## @sc{Example 6:} Finally, a preconditioner which depends on a
## parameter @var{k}.
##
## @example
## @group
##   function y = apply_M (x, varargin)
##   K = varargin@{1@};
##   y = x;
##   y(1:K) = x(1:K)./[1:K]';
##   endfunction
##
##   [x, flag, relres, iter, resvec, eigest] = ...
##        pcg (A, b, [], [], "apply_m", [], [], 3)
## @end group
## @end example
##
## References:
##
## @enumerate
## @item
## C.T. Kelley, @cite{Iterative Methods for Linear and Nonlinear Equations},
## SIAM, 1995. (the base PCG algorithm)
##
## @item
## Y. Saad, @cite{Iterative Methods for Sparse Linear Systems}, PWS 1996.
## (condition number estimate from PCG) Revised version of this book is
## available online at @url{http://www-users.cs.umn.edu/~saad/books.html}
## @end enumerate
##
## @seealso{sparse, pcr}
## @end deftypefn

## Author: Piotr Krzyzanowski <piotr.krzyzanowski@mimuw.edu.pl>
## Modified by: Vittoria Rezzonico <vittoria.rezzonico@epfl.ch>
##    - Add the ability to provide the pre-conditioner as two separate
## matrices

function [x, flag, relres, iter, resvec, eigest] = pcg (A, b, tol, maxit, m1, m2, x0, varargin)

  ## M = M1*M2

  if (nargin < 7 || isempty (x0))
    x = zeros (size (b));
  else
    x = x0;
  endif

  if (nargin < 5 || isempty (m1))
     exist_m1 = 0;
  else
     exist_m1 = 1;
  endif

  if (nargin < 6 || isempty (m2))
     exist_m2 = 0;
  else
     exist_m2 = 1;
  endif

  if (nargin < 4 || isempty (maxit))
    maxit = min (size (b, 1), 20);
  endif

  maxit += 2;

  if (nargin < 3 || isempty (tol))
    tol = 1e-6;
  endif

  preconditioned_residual_out = false;
  if (nargout > 5)
    T = zeros (maxit, maxit);
    preconditioned_residual_out = true;
  endif

  ## Assume A is positive definite.
  matrix_positive_definite = true;

  p = zeros (size (b));
  oldtau = 1;
  if (isnumeric (A))
    ## A is a matrix.
    r = b - A*x;
  else
    ## A should be a function.
    r = b - feval (A, x, varargin{:});
  endif

  resvec(1,1) = norm (r);
  alpha = 1;
  iter = 2;

  while (resvec (iter-1,1) > tol * resvec (1,1) && iter < maxit)
    if (exist_m1)
      if(isnumeric (m1))
        y = m1 \ r;
      else
        y = feval (m1, r, varargin{:});
      endif
    else
      y = r;
    endif
    if (exist_m2)
      if (isnumeric (m2))
        z = m2 \ y;
      else
        z = feval (m2, y, varargin{:});
      endif
    else
      z = y;
    endif
    tau = z' * r;
    resvec (iter-1,2) = sqrt (tau);
    beta = tau / oldtau;
    oldtau = tau;
    p = z + beta * p;
    if (isnumeric (A))
      ## A is a matrix.
      w = A * p;
    else
      ## A should be a function.
      w = feval (A, p, varargin{:});
    endif
    ## Needed only for eigest.
    oldalpha = alpha;
    alpha = tau / (p'*w);
    if (alpha <= 0.0)
      ## Negative matrix.
      matrix_positive_definite = false;
    endif
    x += alpha * p;
    r -= alpha * w;
    if (nargout > 5 && iter > 2)
      T(iter-1:iter, iter-1:iter) = T(iter-1:iter, iter-1:iter) + ...
          [1 sqrt(beta); sqrt(beta) beta]./oldalpha;
      ## EVS = eig(T(2:iter-1,2:iter-1));
      ## fprintf(stderr,"PCG condest: %g (iteration: %d)\n", max(EVS)/min(EVS),iter);
    endif
    resvec (iter,1) = norm (r);
    iter++;
  endwhile

  if (nargout > 5)
    if (matrix_positive_definite)
      if (iter > 3)
        T = T(2:iter-2,2:iter-2);
        l = eig (T);
        eigest = [min(l), max(l)];
        ## fprintf (stderr, "pcg condest: %g\n", eigest(2)/eigest(1));
      else
        eigest = [NaN, NaN];
        warning ("pcg: eigenvalue estimate failed: iteration converged too fast");
      endif
    else
      eigest = [NaN, NaN];
    endif

    ## Apply the preconditioner once more and finish with the precond
    ## residual.
    if (exist_m1)
      if (isnumeric (m1))
        y = m1 \ r;
      else
        y = feval (m1, r, varargin{:});
      endif
    else
      y = r;
    endif
    if (exist_m2)
      if (isnumeric (m2))
        z = m2 \ y;
      else
        z = feval (m2, y, varargin{:});
      endif
    else
      z = y;
    endif

    resvec (iter-1,2) = sqrt (r' * z);
  else
    resvec = resvec(:,1);
  endif

  flag = 0;
  relres = resvec (iter-1,1) ./ resvec(1,1);
  iter -= 2;
  if (iter >= maxit - 2)
    flag = 1;
    if (nargout < 2)
      warning ("pcg: maximum number of iterations (%d) reached\n", iter);
      warning ("the initial residual norm was reduced %g times.\n", ...
               1.0 / relres);
    endif
  elseif (nargout < 2)
    fprintf (stderr, "pcg: converged in %d iterations. ", iter);
    fprintf (stderr, "the initial residual norm was reduced %g times.\n",...
             1.0/relres);
  endif

  if (! matrix_positive_definite)
    flag = 3;
    if (nargout < 2)
      warning ("pcg: matrix not positive definite?\n");
    endif
  endif
endfunction

%!demo
%!
%!      # Simplest usage of pcg (see also 'help pcg')
%!
%!      N = 10;
%!      A = diag ([1:N]); b = rand (N, 1); y =  A \ b; #y is the true solution
%!      x = pcg (A, b);
%!      printf('The solution relative error is %g\n', norm (x - y) / norm (y));
%!
%!      # You shouldn't be afraid if pcg issues some warning messages in this
%!      # example: watch out in the second example, why it takes N iterations
%!      # of pcg to converge to (a very accurate, by the way) solution
%!demo
%!
%!      # Full output from pcg, except for the eigenvalue estimates
%!      # We use this output to plot the convergence history
%!
%!      N = 10;
%!      A = diag ([1:N]); b = rand (N, 1); X =  A \ b; #X is the true solution
%!      [x, flag, relres, iter, resvec] = pcg (A, b);
%!      printf('The solution relative error is %g\n', norm (x - X) / norm (X));
%!      title('Convergence history'); xlabel('Iteration'); ylabel('log(||b-Ax||/||b||)');
%!      semilogy([0:iter], resvec / resvec(1),'o-g');
%!      legend('relative residual');
%!demo
%!
%!      # Full output from pcg, including the eigenvalue estimates
%!      # Hilbert matrix is extremely ill conditioned, so pcg WILL have problems
%!
%!      N = 10;
%!      A = hilb (N); b = rand (N, 1); X = A \ b; #X is the true solution
%!      [x, flag, relres, iter, resvec, eigest] = pcg (A, b, [], 200);
%!      printf('The solution relative error is %g\n', norm (x - X) / norm (X));
%!      printf('Condition number estimate is %g\n', eigest(2) / eigest (1));
%!      printf('Actual condition number is   %g\n', cond (A));
%!      title('Convergence history'); xlabel('Iteration'); ylabel('log(||b-Ax||)');
%!      semilogy([0:iter], resvec,['o-g';'+-r']);
%!      legend('absolute residual','absolute preconditioned residual');
%!demo
%!
%!      # Full output from pcg, including the eigenvalue estimates
%!      # We use the 1-D Laplacian matrix for A, and cond(A) = O(N^2)
%!      # and that's the reasone we need some preconditioner; here we take
%!      # a very simple and not powerful Jacobi preconditioner,
%!      # which is the diagonal of A
%!
%!      N = 100;
%!      A = zeros (N, N);
%!      for i=1 : N - 1 # form 1-D Laplacian matrix
%!              A (i:i+1, i:i+1) = [2 -1; -1 2];
%!      endfor
%!      b = rand (N, 1); X = A \ b; #X is the true solution
%!      maxit = 80;
%!      printf('System condition number is %g\n', cond (A));
%!      # No preconditioner: the convergence is very slow!
%!
%!      [x, flag, relres, iter, resvec, eigest] = pcg (A, b, [], maxit);
%!      printf('System condition number estimate is %g\n', eigest(2) / eigest(1));
%!      title('Convergence history'); xlabel('Iteration'); ylabel('log(||b-Ax||)');
%!      semilogy([0:iter], resvec(:,1), 'o-g');
%!      legend('NO preconditioning: absolute residual');
%!
%!      pause(1);
%!      # Test Jacobi preconditioner: it will not help much!!!
%!
%!      M = diag (diag (A)); # Jacobi preconditioner
%!      [x, flag, relres, iter, resvec, eigest] = pcg (A, b, [], maxit, M);
%!      printf('JACOBI preconditioned system condition number estimate is %g\n', eigest(2) / eigest(1));
%!      hold on;
%!      semilogy([0:iter], resvec(:,1), 'o-r');
%!      legend('NO preconditioning: absolute residual', ...
%!             'JACOBI preconditioner: absolute residual');
%!
%!      pause(1);
%!      # Test nonoverlapping block Jacobi preconditioner: it will help much!
%!
%!      M = zeros (N, N); k = 4;
%!      for i = 1 : k : N # form 1-D Laplacian matrix
%!              M (i:i+k-1, i:i+k-1) = A (i:i+k-1, i:i+k-1);
%!      endfor
%!      [x, flag, relres, iter, resvec, eigest] = pcg (A, b, [], maxit, M);
%!      printf('BLOCK JACOBI preconditioned system condition number estimate is %g\n', eigest(2) / eigest(1));
%!      semilogy ([0:iter], resvec(:,1),'o-b');
%!      legend('NO preconditioning: absolute residual', ...
%!             'JACOBI preconditioner: absolute residual', ...
%!             'BLOCK JACOBI preconditioner: absolute residual');
%!      hold off;
%!test
%!
%!      #solve small diagonal system
%!
%!      N = 10;
%!      A = diag ([1:N]); b = rand (N, 1); X = A \ b; #X is the true solution
%!      [x, flag] = pcg (A, b, [], N+1);
%!      assert(norm (x - X) / norm (X), 0, 1e-10);
%!      assert(flag, 0);
%!
%!test
%!
%!      #solve small indefinite diagonal system
%!      #despite A is indefinite, the iteration continues and converges
%!      #indefiniteness of A is detected
%!
%!      N = 10;
%!      A = diag([1:N] .* (-ones(1, N) .^ 2)); b = rand (N, 1); X = A \ b; #X is the true solution
%!      [x, flag] = pcg (A, b, [], N+1);
%!      assert(norm (x - X) / norm (X), 0, 1e-10);
%!      assert(flag, 3);
%!
%!test
%!
%!      #solve tridiagonal system, do not converge in default 20 iterations
%!
%!      N = 100;
%!      A = zeros (N, N);
%!      for i = 1 : N - 1 # form 1-D Laplacian matrix
%!              A (i:i+1, i:i+1) = [2 -1; -1 2];
%!      endfor
%!      b = ones (N, 1); X = A \ b; #X is the true solution
%!      [x, flag, relres, iter, resvec, eigest] = pcg (A, b, 1e-12);
%!      assert(flag);
%!      assert(relres > 1.0);
%!      assert(iter, 20); #should perform max allowable default number of iterations
%!
%!test
%!
%!      #solve tridiagonal system with 'prefect' preconditioner
%!      #converges in one iteration, so the eigest does not work
%!      #and issues a warning
%!
%!      N = 100;
%!      A = zeros (N, N);
%!      for i = 1 : N - 1 # form 1-D Laplacian matrix
%!              A (i:i+1, i:i+1) = [2 -1; -1 2];
%!      endfor
%!      b = ones (N, 1); X = A \ b; #X is the true solution
%!      [x, flag, relres, iter, resvec, eigest] = pcg (A, b, [], [], A, [], b);
%!      assert(norm (x - X) / norm (X), 0, 1e-6);
%!      assert(flag, 0);
%!      assert(iter, 1); #should converge in one iteration
%!      assert(isnan (eigest), isnan ([NaN, NaN]));
%!