[SCIP] unresolved numerical troubles

[James Cussens]

Hi Marc,

Yes, using IPOPT is a good idea. I've used IPOPT before on similar instances (not ones with numerical problems) and, much to my surprise, solving was slower. But avoiding
these numerical problems is more important and perhaps I can get to the bottom of (and hopefully avoid) the performance problem.

Thanks,

James

James Cussens
Dept of Computer Science, University of Bristol
https://jcussens.github.io/
Funded PhDs available in Bristol in the following areas: Data Science<http://www.bristol.ac.uk/cdt/compass/>, Interactive AI<http://www.bristol.ac.uk/cdt/interactive-ai/>, Cyber Security<http://www.bristol.ac.uk/cdt/cyber-security/> or Digital Health<http://www.bristol.ac.uk/cdt/digital-health/>.
________________________________
From: Scip <scip-bounces at zib.de> on behalf of Marc Pfetsch <pfetsch at mathematik.tu-darmstadt.de>
Sent: 26 February 2021 10:51
To: scip at zib.de <scip at zib.de>
Subject: Re: [SCIP] unresolved numerical troubles



Hi James,

unresolved numerical troubles, means that all efforts of SCIP to get a
feasible and optimal LP solution were fruitless. SCIP tries to tighten
tolerances, changes the simplex method, restarts from scratch etc. in
order to do so. If the LP solver fails to produce a solution, SCIP has
to stop.

In your particular case, there seems to be one simple thing to improve
your situation: You should build SCIP with IPOPT - this solves your
instance for me. IPOPT helps with finding feasible solutions, which
seems to stablize the process.

Best

Marc



On 26/02/2021 10.05, James Cussens wrote:
> Hi all,
>
> I use a subMIQP for a pricer I have. Sometimes when I run it I get
>
> [solve.c:3912] ERROR: (node 1) unresolved numerical troubles in LP 32
> cannot be dealt with
>
> I am keen to understand what might have led to such (fatal!) problems
> and if I can avoid them. I attach an example of this problem. In the
> attached file you have the original problem and the output when solving
> the (presolved, transformed problem). The odd thing is that, in the
> example I have attached, after presolving, the problem should not be too
> hard to solve: all variables are continuous and we only have convex
> constraints (22 second-order cone and 21 linear).
>
> One of my variables is fixed to a small positive value, perhaps that has
> something to do with it.
>
> I am using SoPlex as the LP solver.
>
> James
>
> James Cussens
> Dept of Computer Science, University of Bristol
> https://jcussens.github.io/
> Funded PhDs available in Bristol in the following areas: Data Science
> <http://www.bristol.ac.uk/cdt/compass/>, Interactive AI
> <http://www.bristol.ac.uk/cdt/interactive-ai/>, Cyber Security
> <http://www.bristol.ac.uk/cdt/cyber-security/> or Digital Health
> <http://www.bristol.ac.uk/cdt/digital-health/>.
>
> _______________________________________________
> Scip mailing list
> Scip at zib.de
> https://listserv.zib.de/mailman/listinfo/scip
>
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