[Ipopt] Sparse Hessian approximation

Andreas Waechter awaechter.iems at gmail.com
Mon Sep 12 20:13:20 EDT 2011

Hi Sebastian,

Please reply to the Ipopt mailing list - you get answers that are better 
than mine...

I don't know of any optimization algorithm in C++ that can take do what 
you want.

As far as I know, Lancelot, using partial separability, can do 
structured quasi-Newton updates that makes use of sparsity.  But it is 
written in Fortran, requires a different interface, and uses quite a 
different algorithm.



On 09/12/2011 07:03 PM, lengagne sebastien wrote:
> Dear Andreas,
> Thank you for your answer.
> In fact the hessian has half of the value non zero is the woth cases 
> (when I consider only 100 or 200 variables).
> When I use 3000 variables, I think there is only 10000 or 100000 
> values that are non null.
> So I understand that Ipopt does not allow me to do that.
> Maybe you know another algorithm in C++ that can do it.
> Best regards
> sebastien
> Le 13/09/2011 00:30, Andreas Waechter a écrit :
>> Hi Sébastien,
>> Sorry, there is not way to specify a sparsity structure for the 
>> Hessian approximation.  By the way, Ipopt uses the limited-memory 
>> BFGS approximation, i.e., it never stores the full dense Hessian 
>> anyway.  And if half of the entries in the Hessian are zero, this is 
>> not particularly sparse at all!  Usually, you would call a matrix 
>> sparse if it is has only a few nonzeros per row/column.
>> Hope this helps,
>> Andreas
>> On 09/12/2011 04:06 AM, lengagne sebastien wrote:
>>> Hi every body,
>>> I am using IPOPT with more than 3000 variables and more than 5000 
>>> constraints.
>>> I cannot compute the Hessian (too long computation time), 
>>> nevertheless I know its structure. It is very sparse (more than the 
>>> halk is set with zero).
>>> Is it possible to use BFGS method and to specify the non null value, 
>>> that way it could be faster and more efficient
>>> thank you
>>> Sébastien
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