[MITgcm-support] tutorial_global_oce_optim optimisation failed

[Andrew McRae]

Right, but the cost function is the same value each time, the norm of x is
0 each time, and the norm of g is the same each time.  This suggests
nothing is happening.  It's a bit ridiculous that one of the core tutorials
simply isn't working out of the box...

I will have a go at debugging.

Andrew

On 30 April 2018 at 22:54, Matthew Mazloff <mmazloff at ucsd.edu> wrote:

> Well you are correct that its not actually taking a step because the dot
> product of the control is 0:
>
> norm of x................... 0.00000000E+00
>>
>> meaning the controls are all 0 still.
>
> However the gradients are non-zero
>
> norm of g................... 0.12730927E-01
>>
>> so the linesearch should step and
> ecco_ctrl_MIT_CE_000.opt0001
> should not be all zero.
>
> To debug this you could put a print statement in optim_writedata.F to see
> what it is writing…..
>
> I don’t know enough about this tutorial to be a bigger help, sorry
>
> Matt
>
>
> On Apr 30, 2018, at 2:50 PM, Andrew McRae <andrew.mcrae at physics.ox.ac.uk>
> wrote:
>
> Yes, I did.
>
> On 30 April 2018 at 22:42, Matthew Mazloff <mmazloff at ucsd.edu> wrote:
>
>> This is still iteration 0. You have to update data.optim to tell it you
>> are now at iteration 1
>>
>> Matt
>>
>>
>> On Apr 30, 2018, at 2:38 PM, Andrew McRae <andrew.mcrae at physics.ox.ac.uk>
>> wrote:
>>
>> I tried a few steps of this, but the output of optim.x always has
>>
>>   cost function............... 0.62002323E+01
>>   norm of x................... 0.00000000E+00
>>   norm of g................... 0.12730927E-01
>>
>> near the end, with no decrease in the cost function.  So I guess it's not
>> actually taking the step?
>>
>> Andrew
>>
>> On 27 April 2018 at 18:04, Andrew McRae <andrew.mcrae at physics.ox.ac.uk>
>> wrote:
>>
>>> !!!  Okay...
>>>
>>> Yes, it produced the .opt0001 file.  I'll see how this goes.
>>>
>>> Thanks,
>>> Andrew
>>>
>>> On 27 April 2018 at 17:57, Matthew Mazloff <mmazloff at ucsd.edu> wrote:
>>>
>>>> Hello
>>>>
>>>> Its been awhile, but I am pretty sure that is the normal output. It
>>>> says “fail", but it did give you a new and ecco_ctrl_MIT_CE_000.opt0001
>>>> (correct?) and if you unpack and run likely the cost will descend.
>>>>
>>>> I think it worked correctly. lsopt/optim are just confusing…but I think
>>>> its working. I think all is good!
>>>>
>>>> Matt
>>>>
>>>>
>>>>
>>>> On Apr 27, 2018, at 8:25 AM, Andrew McRae <
>>>> andrew.mcrae at physics.ox.ac.uk> wrote:
>>>>
>>>> Just separating this from the other thread
>>>> <http://mailman.mitgcm.org/pipermail/mitgcm-support/2018-April/011521.html>,
>>>> I got the bundled MITgcm optim routine built (having made these changes
>>>> <https://github.com/MITgcm/MITgcm/compare/master...dorugeber:optim_fix>,
>>>> based on this
>>>> <http://mailman.mitgcm.org/pipermail/mitgcm-support/2010-September/006825.html>
>>>> thread from 2010 and this
>>>> <http://mailman.mitgcm.org/pipermail/mitgcm-support/2016-July/010527.html>
>>>> one from 2016).
>>>>
>>>> I use OpenAD to create the adjoint.
>>>>
>>>> My steps are:
>>>> 1) in the build directory, run ../../../tools/genmake2 -oad
>>>> -mods=../code_oad
>>>> 2) run make depend and make adAll
>>>> 3) copy input_oad/ into a new folder scratch/
>>>> 4) within scratch/, run ./prepare_run
>>>> 5) copy mitgcmuv_ad from build/ into scratch/, copy optim.x into
>>>> scratch/OPTIM/
>>>> 6) run ./mitgcmuv_ad
>>>> 7) in scratch/OPTIM, create symlinks to ../data.optim and ../data.ctrl
>>>> 8) copy the files ecco_cost_MIT_CE_000.opt0000 and
>>>> ecco_ctrl_MIT_CE_000.opt0000 into the OPTIM subdirectory
>>>> 9) run ./optim.x within the subdirectory
>>>>
>>>> The full output is attached, but I assume the optimisation failed since
>>>> the last lines are
>>>>
>>>>   optimization stopped because :
>>>>   ifail =   4    the search direction is not a descent one
>>>>
>>>> Any ideas?  (I guess this isn't something that is tested in the daily
>>>> builds?)
>>>>
>>>> In the meantime, I'll try the m1qn3 routine as in the other thread,
>>>> which should help distinguish between a problem with the optimisation
>>>> routine or the gradient generated by mitgcmuv_ad.
>>>>
>>>> Andrew
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>>>>
>>>>
>>>>
>>>
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