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

[Vivek Periaraj]

In CPLEX multi-threading environment, there are two modes - deterministic and opportunistic. In the deterministic mode, the algorithm would follow the same path between each identical runs, hence the solution vector would remain the same. In the other mode, this is not guaranteed. If you use MPI for this, then I guess you could manually enforce such similar deterministic behavior. 


Regards, 
Vivek. 

----- Original Message ----- 

From: "AliReza Khoshgoftar Monfared" <khoshgoftar at gmail.com> 
To: ipopt at list.coin-or.org, "jonathan hogg" <jonathan.hogg at stfc.ac.uk>, "vivek periaraj" <vivek.periaraj at gmail.com> 
Sent: Monday, December 17, 2012 10:32:34 PM 
Subject: Re: IpOpt code: Detereministic or Random across various runs? 




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'; 
> _______________________________________________ 
> Ipopt mailing list 
> Ipopt at list.coin-or.org 
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Scanned by iCritical. 







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

<blockquote>

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: 



<blockquote>
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'; 
</blockquote>

</blockquote>