[Ipopt-tickets] [Ipopt] #110: Scaling and convergence check

[Ipopt]

#110: Scaling and convergence check
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  Reporter:  hmartin            |       Owner:  ipopt-team
      Type:  defect             |      Status:  closed    
  Priority:  normal             |   Component:  Ipopt     
   Version:  3.7 (C++ Version)  |    Severity:  normal    
Resolution:  fixed              |    Keywords:            
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Changes (by andreasw):

  * status:  new => closed
  * resolution:  => fixed


Comment:

 Hi Herve,

 I added a new option called nlp_scaling_min_value that is now defaulted to
 1e-8, to do what you suggested.  I'm not 100% convinced that this is a
 good idea, but I agree that scaling factors that are that extreme are not
 very useful.

 The idea of the gradient-based scaling is that the derivative values give
 an indication of the scaling of the problem, and that it is a good idea to
 have them at most of size 100.  So, if there is a derivative at the
 starting point that has a value of 1e16, then the assumption is that this
 constraint it "overscaled" by 1e14.  If this assumption is not correct,
 then it does not make sense to use the gradient-based scaling method.  You
 should either scale your problem in the formulation and switch off the
 scaling, or you can provide your own scaling parameters (as you did in
 your modified hs071_cpp example).

 I also fixed the printout of the "modified d_L" etc in the output, this
 was indeed incorrect.  But, luckily, it was only output, and did not
 affect the actual computations in any way. :-)

 Thanks again,

 Andreas

-- 
Ticket URL: <https://projects.coin-or.org/Ipopt/ticket/110#comment:1>
Ipopt <http://projects.coin-or.org/Ipopt>
Interior-point optimizer for nonlinear programs.