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[Johan Hake <745903@xxxxxxxxxxxxxxxxxx>]

A standard way is to use get and set  local, taking into account that
you then only will get the local data. In python does the array() method
map to get_local so that would be the preferred way in C++ I guess.
Garth could probably fill in on what is safe and what is not safe wrt
parallel vectors and get_local and set_local.

As I mentioned, above it should be possible to check for same local and
global vector size and instead of creating a new PETSc vector just
assign into the already existing one. Then we could circumvent the
awkward way off assigning vectors from another one as described here.

I close this bug as invalid, btw.

Johan

** Changed in: dolfin
       Status: New => Invalid

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https://bugs.launchpad.net/bugs/745903

Title:
  PETScKrylovSolver fails to iterate with PETScKrylovMatrix

Status in DOLFIN:
  Invalid

Bug description:
  When using PETScKrylovSolver with a virtual PETScKrylovMatrix, the
  solver returns the right hand side without actually iterating. Setting
  "monitor_convergence" to True shows that after the first iteration,
  the preconditioned residual is zero, although no preconditioner is
  used. (The "true residual norm" is correctly given as nonzero.)

  This happens both in Python and in C++, and for all available Krylov
  solvers, but not for all such matrices. It seems to be related to the
  way the return argument is passed.

  Below is a minimal Python example with one working and one non-working
  PETScKrylovMatrix.

  ###
  from dolfin import *

  def boundary(x,on_boundary):
      return on_boundary

  mesh = UnitSquare(32, 32)
  V = FunctionSpace(mesh, 'CG', 1)

  bc = DirichletBC(V, Constant(0.0), boundary)
  u = TrialFunction(V); v = TestFunction(V); 
  A, b = assemble_system( inner(grad(u), grad(v))*dx, Constant(1.0)*v*dx, bc)

  class NewtonMatrix(PETScKrylovMatrix) :
      def __init__(self) :
          PETScKrylovMatrix.__init__(self, V.dim(), V.dim())

      def mult(self, *args) :  
          x = args[0]; bc.apply(x)
          solve(A,args[1],x)

  class NewtonMatrix2(PETScKrylovMatrix) :
      def __init__(self) :
          PETScKrylovMatrix.__init__(self, V.dim(), V.dim())

      def mult(self, *args) :  
          x = args[0]; bc.apply(x)
          y = Function(V)
          solve(A,y.vector(),x)
          args[1][:] = y.vector()[:]

  NewtonSolver = PETScKrylovSolver('cg', 'none')
  NewtonSolver.parameters["monitor_convergence"] = True

  y = Function(V)
  solve(A,y.vector(),b)

  x_petsc = PETScVector(V.dim())

  print NewtonSolver.solve(NewtonMatrix(), x_petsc, down_cast(y.vector()))
  print (b - x_petsc).norm('l2') # works: this is zero 

  x_petsc.zero()

  print NewtonSolver.solve(NewtonMatrix2(), x_petsc, down_cast(b)) # only one iteration
  print (y.vector() - x_petsc).norm('l2') # doesn't work: this is not zero
  print (b - x_petsc).norm('l2') # but this is

  x_petsc.zero()

  print NewtonSolver.solve(NewtonMatrix2(), x_petsc, down_cast(b)) # only one iteration
  print (y.vector() - x_petsc).norm('l2') # doesn't work: this is not zero
  print (b - x_petsc).norm('l2') # but this is