On Tue, Oct 2, 2018 at 5:26 PM Weizhuo Wang <weizh...@illinois.edu> wrote:
> I didn't specify a tolerance, it was using the default tolerance. Doesn't
> the asymptoting norm implies finer grid won't help to get finer solution?
>
There are two things going on in your test, discretization error controlled
by the grid, and algebraic error controlled by the solver. This makes it
difficult to isolate what is happening. However, it seems clear that your
plot is looking at algebraic error. You can confirm this by using
-pc_type lu
for the solve. Then all you have is discretization error.
Thanks,
Matt
> Mark Adams <mfad...@lbl.gov> 于2018年10月2日周二 下午4:11写道：
>
>>
>>
>> On Tue, Oct 2, 2018 at 5:04 PM Weizhuo Wang <weizh...@illinois.edu>
>> wrote:
>>
>>> Yes I was using one norm in my Helmholtz code, the example code used 2
>>> norm. But now I am using 2 norm in both code.
>>>
>>> /*
>>> Check the error
>>> */
>>> ierr = VecAXPY(x,-1.0,u); CHKERRQ(ierr);
>>> ierr = VecNorm(x,NORM_1,&norm); CHKERRQ(ierr);
>>> ierr = KSPGetIterationNumber(ksp,&its); CHKERRQ(ierr);
>>> ierr = PetscPrintf(PETSC_COMM_WORLD,"Norm of error %g iterations
>>> %D\n",(double)norm/(m*n),its); CHKERRQ(ierr);
>>>
>>> I made a plot to show the increase:
>>>
>>
>>
>> FYI, this is asymptoting to a constant. What solver tolerance are
>> you using?
>>
>>
>>>
>>> [image: Norm comparison.png]
>>>
>>> Mark Adams <mfad...@lbl.gov> 于2018年10月2日周二 下午2:27写道：
>>>
>>>>
>>>>
>>>> On Tue, Oct 2, 2018 at 2:24 PM Weizhuo Wang <weizh...@illinois.edu>
>>>> wrote:
>>>>
>>>>> The example code and makefile are attached below. The whole thing
>>>>> started as I tried to build a Helmholtz solver, and the mean error
>>>>> (calculated by: sum( | numerical_sol - analytical_sol | / analytical_sol )
>>>>> )
>>>>>
>>>>
>>>> This is a one norm. If you use max (instead of sum) then you don't need
>>>> to scale. You do have to be careful about dividing by (near) zero.
>>>>
>>>>
>>>>> increases as I use finer and finer grids.
>>>>>
>>>>
>>>> What was the rate of increase?
>>>>
>>>>
>>>>> Then I looked at the example 12 (Laplacian solver) which is similar to
>>>>> what I did to see if I have missed something. The example is using 2_norm.
>>>>> I have made some minor modifications (3 places) on the code, you can
>>>>> search
>>>>> 'Modified' in the code to see them.
>>>>>
>>>>> If this helps: I configured the PETSc to use real and double
>>>>> precision. Changed the name of the example code from ex12.c to ex12c.c
>>>>>
>>>>> Thanks for all your reply!
>>>>>
>>>>> Weizhuo
>>>>>
>>>>>
>>>>> Smith, Barry F. <bsm...@mcs.anl.gov>
>>>>>
>>>>>
>>>>>> Please send your version of the example that computes the mean
>>>>>> norm of the grid; I suspect we are talking apples and oranges
>>>>>>
>>>>>> Barry
>>>>>>
>>>>>>
>>>>>>
>>>>>> > On Oct 1, 2018, at 7:51 PM, Weizhuo Wang <weizh...@illinois.edu>
>>>>>> wrote:
>>>>>> >
>>>>>> > I also tried to divide the norm by m*n , which is the number of
>>>>>> grids, the trend of norm still increases.
>>>>>> >
>>>>>> > Thanks!
>>>>>> >
>>>>>> > Weizhuo
>>>>>> >
>>>>>> > Matthew Knepley <knep...@gmail.com>
>>>>>> > On Mon, Oct 1, 2018 at 6:31 PM Weizhuo Wang <weizh...@illinois.edu>
>>>>>> wrote:
>>>>>> > Hi!
>>>>>> >
>>>>>> > I'm recently trying out the example code provided with the KSP
>>>>>> solver (ex12.c). I noticed that the mean norm of the grid increases as I
>>>>>> use finer meshes. For example, the mean norm is 5.72e-8 at m=10 n=10.
>>>>>> However at m=100, n=100, mean norm increases to 9.55e-6. This seems
>>>>>> counter
>>>>>> intuitive, since most of the time error should decreases when using finer
>>>>>> grid. Am I doing this wrong?
>>>>>> >
>>>>>> > The norm is misleading in that it is the l_2 norm, meaning just the
>>>>>> sqrt of the sum of the squares of
>>>>>> > the vector entries. It should be scaled by the volume element to
>>>>>> approximate a scale-independent
>>>>>> > norm (like the L_2 norm).
>>>>>> >
>>>>>> > Thanks,
>>>>>> >
>>>>>> > Matt
>>>>>> >
>>>>>> > Thanks!
>>>>>> > --
>>>>>> > Wang Weizhuo
>>>>>> >
>>>>>> >
>>>>>> > --
>>>>>> > What most experimenters take for granted before they begin their
>>>>>> experiments is infinitely more interesting than any results to which
>>>>>> their
>>>>>> experiments lead.
>>>>>> > -- Norbert Wiener
>>>>>> >
>>>>>> > https://www.cse.buffalo.edu/~knepley/
>>>>>> <https://urldefense.proofpoint.com/v2/url?u=https-3A__www.cse.buffalo.edu_-7Eknepley_&d=DwMFaQ&c=OCIEmEwdEq_aNlsP4fF3gFqSN-E3mlr2t9JcDdfOZag&r=hsLktHsuxNfF6zyuWGCN8x-6ghPYxhx4cV62Hya47oo&m=KjmDEsZ6w8LEry7nlv3Bw7-pczqWbKGueFU59VoIWZg&s=tEv9-AHhL2CIlmmVos0gFa5PAY9oMG3aTQlnfi62ivA&e=>
>>>>>> >
>>>>>> >
>>>>>> > --
>>>>>> > Wang Weizhuo
>>>>>>
>>>>>>
>>>>>
>>>>> --
>>>>> Wang Weizhuo
>>>>>
>>>>
>>>
>>> --
>>> Wang Weizhuo
>>>
>>
>
> --
> Wang Weizhuo
>
--
What most experimenters take for granted before they begin their
experiments is infinitely more interesting than any results to which their
experiments lead.
-- Norbert Wiener
https://www.cse.buffalo.edu/~knepley/ <http://www.cse.buffalo.edu/~knepley/>
