Thanks, after adding the code before solving, it works.
I do not exactly understand what you mean by "time-stepping solvers". This problem exists only in sfepy2015.1 or also in sfepy-2014.4? Do you mean a interactive building problem with codes like "pb.time_update, pb.update_materials" does not not work well currently? I am worrying about the influence of this "time-stepping solvers". Where can I find the fix? also in sfepy-devel?
在 2015年3月13日星期五 UTC+1下午1:42:26，Robert Cimrman写道：The linear option works in connection with time-stepping solvers. I have found
recently, that the time stepping solvers do not play well in interactive
building of the problem. The fix should be available soon.
The singular matrix warning is caused by the fact, that in the linear case the
non-linear solver assumes that the matrix is already assembled (which is the
case, if called from the time-stepping solvers in sfepy). In your script, try
calling the following before solving the problem:
# pb = Problem('elasticity', equations=eqs, nls=nls, ls=ls)
from sfepy.solvers.ts_solvers import prepare_matrix
prepare_matrix(pb, pb.create_state())
r.
On 03/12/2015 06:01 PM, Ronghai Wu wrote:
> I wrote my codes in sfepy-2014.4, but recently I upgraded to sfepy-2015.1.
> If every other things are the same except for solvers, I got some cases
> work and some do not. Do you have any idea about the reason?
> (1) If I keep the problem as nonlinear, it works(original code)
> ls = ScipyDirect({'presolve':True})
> nls_status = IndexedStruct()
> nls = Newton({'problem' : 'nonlinear'}, lin_solver=ls, status=nls_status)
> pb = Problem('elasticity', equations=eqs, nls=nls, ls=ls)
> #############################################################
> sfepy: nls: iter: 0, residual: 2.785678e+02 (rel: 1.000000e+00)
> sfepy: rezidual: 0.46 [s]
> sfepy: solve: 57.95 [s]
> sfepy: matrix: 0.67 [s]
> sfepy: nls: iter: 1, residual: 2.625559e-11 (rel: 9.425207e-14)
>
>
> (2) if I change to linear, it does not work.
> ls = ScipyDirect({'presolve':True})
> nls_status = IndexedStruct()
> nls = Newton({'is_linear' : True}, lin_solver=ls, status=nls_status)
> pb = Problem('elasticity', equations=eqs, nls=nls, ls=ls)
> #################################################
> sfepy: nls: iter: 0, residual: 2.785678e+02 (rel: 1.000000e+00)
> virtual201503/local/lib/python2.7/site-packages/scipy/sparse/linalg/dsolve/linsolve.py:145: MatrixRankWarning: Matrix is exactly singular
> warn("Matrix is exactly singular", MatrixRankWarning)
> sfepy: rezidual: 0.47 [s]
> sfepy: solve: 19.58 [s]
> sfepy: matrix: 0.00 [s]
> sfepy: linesearch: iter 1, (nan < 2.78565e+02) (new ls: 1.000000e-01)
> sfepy: linesearch: iter 1, (nan < 2.78565e+02) (new ls: 1.000000e-02)
> sfepy: linesearch: iter 1, (nan < 2.78565e+02) (new ls: 1.000000e-03)
> sfepy: linesearch: iter 1, (nan < 2.78565e+02) (new ls: 1.000000e-04)
> sfepy: linesearch: iter 1, (nan < 2.78565e+02) (new ls: 1.000000e-05)
> sfepy: linesearch: iter 1, (nan < 2.78565e+02) (new ls: 1.000000e-06)
> sfepy: linesearch: iter 1, (nan < 2.78565e+02) (new ls: 1.000000e-07)
> sfepy: linesearch failed, continuing anyway
> sfepy: nls: iter: 1, residual: nan (rel: nan)
>
> (3)
>
> ls = PETScKrylovSolver({'method' : 'cg', 'precond' : 'icc', 'eps_r' : 1e-8, 'i_max' : 100})
> nls_status = IndexedStruct()
> nls = Newton({'is_linear' : True}, lin_solver=ls, status=nls_status)
> pb = Problem('elasticity', equations=eqs, nls=nls, ls=ls)
> #######################################################
> sfepy: nls: iter: 0, residual: 2.785678e+02 (rel: 1.000000e+00)
> sfepy: cg(icc) convergence: -10 (DIVERGED_INDEFINITE_MAT)
> sfepy: rezidual: 0.47 [s]
> sfepy: solve: 0.26 [s]
> sfepy: matrix: 0.00 [s]
> sfepy: warning: linear system solution precision is lower
> sfepy: then the value set in solver options! (err = 2.785678e+02 < 1.000000e-10)
> sfepy: linesearch: iter 1, (2.78568e+02 < 2.78565e+02) (new ls: 1.000000e-01)
> sfepy: linesearch: iter 1, (2.78568e+02 < 2.78565e+02) (new ls: 1.000000e-02)
> sfepy: linesearch: iter 1, (2.78568e+02 < 2.78565e+02) (new ls: 1.000000e-03)
> sfepy: linesearch: iter 1, (2.78568e+02 < 2.78565e+02) (new ls: 1.000000e-04)
> sfepy: linesearch: iter 1, (2.78568e+02 < 2.78565e+02) (new ls: 1.000000e-05)
> sfepy: linesearch: iter 1, (2.78568e+02 < 2.78565e+02) (new ls: 1.000000e-06)
> sfepy: linesearch: iter 1, (2.78568e+02 < 2.78565e+02) (new ls: 1.000000e-07)
> sfepy: linesearch failed, continuing anyway
> sfepy: nls: iter: 1, residual: 2.785678e+02 (rel: 1.000000e+00)
> sfepy: cg(icc) convergence: -10 (DIVERGED_INDEFINITE_MAT)
> sfepy: rezidual: 0.29 [s]
> sfepy: solve: 0.26 [s]
> sfepy: matrix: 0.00 [s]
> sfepy: warning: linear system solution precision is lower
> sfepy: then the value set in solver options! (err = 2.785678e+02 < 1.000000e-10)
> sfepy: linesearch: iter 2, (2.78568e+02 < 2.78565e+02) (new ls: 1.000000e-01)
> sfepy: linesearch: iter 2, (2.78568e+02 < 2.78565e+02) (new ls: 1.000000e-02)
> sfepy: linesearch: iter 2, (2.78568e+02 < 2.78565e+02) (new ls: 1.000000e-03)
> sfepy: linesearch: iter 2, (2.78568e+02 < 2.78565e+02) (new ls: 1.000000e-04)
> sfepy: linesearch: iter 2, (2.78568e+02 < 2.78565e+02) (new ls: 1.000000e-05)
> sfepy: linesearch: iter 2, (2.78568e+02 < 2.78565e+02) (new ls: 1.000000e-06)
> sfepy: linesearch: iter 2, (2.78568e+02 < 2.78565e+02) (new ls: 1.000000e-07)
> sfepy: linesearch failed, continuing anyway
> sfepy: nls: iter: 2, residual: 2.785678e+02 (rel: 1.000000e+00)
> sfepy: equation "tmp":
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> 在 2015年3月12日星期四 UTC+1下午2:50:25，Robert Cimrman写道：
>>
>> On 03/11/2015 08:02 PM, Ronghai Wu wrote:
>>> Yes, it is linear elasticity problem.
>>> If using linear Newton solver, do you exactly mean ?
>>> ls = ScipyDirect({})
>>> nls_status = IndexedStruct()
>>> nls = Newton({'problem' : 'linear'}, lin_solver=ls, status=nls_status)
>>> pb = Problem('elasticity', equations=eqs, nls=nls, ls=ls)
>>
>> Yes, sorry, I forgot your script is interactive. In SfePy prior to 2015.1
>> the
>> option is {'problem' : 'linear'}. In 2015.1 it reads {'is_linear' : True},
>> as
>> 'problem' was ambiguous.
>>
>>> and if using PETScKrylovSolver, do you exactly mean ?
>>> ls = PETScKrylovSolver({})
>>
>> Essentially, yes. The above uses the default solver, preconditioner,
>> tolerances
>> and other options. You may need to experiment with those. Try, for
>> example:
>>
>> ls = PETScKrylovSolver({
>> 'method' : 'cg',
>> 'precond' : 'icc'
>> 'eps_r' : 1e-8,
>> 'i_max' : 100,
>> })
>>
>> and use this as in your snippet above instead of ScipyDirect.
>>
>>> Another question in the ''Interactive Example: Linear Elasticity'' :
>>> In [23]: bc_fun = Function(’shift_u_fun’, shift_u_fun,
>> extra_args={’shift’ : 0.01})
>>> In [24]: shift_u = EssentialBC(’shift_u’, gamma2, {’u.0’ : bc_fun})
>>> is this shift a necessity to get correct result? I mean without this, if
>> the result will be ridiculous or basically right?
>>> Thanks
>>
>> Here, the shift is the amplitude of the imposed Dirichlet conditions on
>> gamma2. It serves as an example of how to pass extra arguments to the ebc
>> functions. If you set shift to zero, it would be equivalent to set {'u.0'
>> :
>> 0.0} on the gamma2 boundary. Did I answer your question?
>>
>> r.
>>
>>
>