[Ipopt] IpOpt code: Detereministic or Random across various runs?

[AliReza Khoshgoftar Monfared]

Thanks Vivek and Jonathan,

I also suspect that this issue has something to do with MATLAB (or settings
of BLAS and LAPACK).
My original MATLAB code should be single-threaded, but I am suspicious that
when I call Ipopt(), which uses the corresponding mex file, there might be
multiple threads involved.
Is there a way to force a single thread in MATLAB though?

Alireza


Might also be worth seeing if you can force everything to run
> single-threaded and seeing if that has any effect. Some parallel
> algorithms will produce different results on different runs, and even
> small changes in the descend direction can produce different results as
> you describe.
> Jonathan.
> On 15/12/12 11:52, Vivek Periaraj wrote:
> >* Maybe the data structures interfaced from MATLAB to IPOPT are in
> different orders in each run? Sometimes the order in which the variables
> are created affect the solution the algorithm converges to in the end.
> Especially true when multiple optimal solutions exists.
> *>
> >* Regards,
> *>* Vivek
> *>
> >
> >* Hi,
> *>
> >* I have been trying to solve an optimization with Ipopt.
> *>
> >* I'm using the MATLAB interface of Ipopt, and my optimization problem
> is a
> *>* not so complicated quadratic problem satisfying a number of distance
> *>* inequalities for some points.
> *>
> >* I have noticed that in different cases of my problem, whether it is
> solved
> *>* (status 0), detected infeasible (status 2) or exceeded maximum
> iterations
> *>* (status -2), even if I run the code with the exact same initial point
> and
> *>* exact same options, I get results that are different.
> *>* Now, I know that my problem does not have a unique solution, e.g. in
> case
> *>* of instances that are solved completely I get different results while
> all
> *>* satisfy the constraints. But still, I expect ipopt to give
> *>* me similar results when I run it with similar conditions.
> *>
> >* Is there any option in Ipopt that accounts to randomness. Or is it a
> *>* behavior of MATLAB?
> *>
> >* I have noticed that building Ipopt with various versions of BLAS and
> LAPACK
> *>* does not affect this in my case (I tried third party versions
> provided, and
> *>* also ATLAS implementations of libraries in Liunx), but forcing MALTAB
> to
> *>* use different BLAS and LAPACK versions (through environment variables
> *>* BLAS_VERSION and LAPACK_VERSION) has an effect (although in no case,
> I get
> *>* exactly similar results) .
> *>
> >* Has anybody else had such an experience with IpOpt? Should I change a
> *>* setting in Ipopt ot MATLAB?
> *>
> >* Thanks
> *>* ALireza
> *>
> >
> >* PS: If it helps, here is a summary of Ipopt options I have changed:
> *>
> >* ipopt.hessian_approximation = 'limited-memory';
> *>>* ipopt.mu_strategy = 'adaptive'; > tols = [1e-6; Options.MaxR;
> Options.MaxR]; > ipopt.tol = sum(tols); > ipopt.constr_viol_tol = tols(1);
> > ipopt.compl_inf_tol = tols(2); > ipopt.dual_inf_tol = tols(3); >
> hessian_constant = 'yes'; > ipopt.warm_start_init_point = 'yes';
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> Scanned by iCritical.
>
>

On Fri, Dec 14, 2012 at 7:11 PM, AliReza Khoshgoftar Monfared <
khoshgoftar at gmail.com> wrote:

> Hi,
>
> I have been trying to solve an optimization with Ipopt.
>
> I'm using the MATLAB interface of Ipopt, and my optimization problem is a
> not so complicated quadratic problem satisfying a number of distance
> inequalities for some points.
>
> I have noticed that in different cases of my problem, whether it is solved
> (status 0), detected infeasible (status 2) or exceeded maximum iterations
> (status -2), even if I run the code with the exact same initial point and
> exact same options, I get results that are different.
> Now, I know that my problem does not have a unique solution, e.g. in case
> of instances that are solved completely I get different results while all
> satisfy the constraints. But still, I expect ipopt to give
> me similar results when I run it with similar conditions.
>
> Is there any option in Ipopt that accounts to randomness. Or is it a
> behavior of MATLAB?
>
> I have noticed that building Ipopt with various versions of BLAS and
> LAPACK does not affect this in my case (I tried third party versions
> provided, and also ATLAS implementations of libraries in Liunx),
> but forcing MALTAB to use different BLAS and LAPACK versions (through
> environment variables BLAS_VERSION and LAPACK_VERSION) has an effect
> (although in no case, I get exactly similar results) .
>
> Has anybody else had such an experience with IpOpt? Should I change a
> setting in Ipopt ot MATLAB?
>
> Thanks
> ALireza
>
>
> PS: If it helps, here is a summary of Ipopt options I have changed:
>
> ipopt.hessian_approximation = 'limited-memory';
>> ipopt.mu_strategy             =  'adaptive';
>> tols = [1e-6; Options.MaxR; Options.MaxR];
>> ipopt.tol                              =  sum(tols);
>> ipopt.constr_viol_tol         =  tols(1);
>> ipopt.compl_inf_tol           =  tols(2);
>> ipopt.dual_inf_tol              =  tols(3);
>> hessian_constant             =  'yes';
>> ipopt.warm_start_init_point = 'yes';
>
>
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