[Ipopt] Lagrange multipliers for inequality (<=) constraints

[Stefan Vigerske]

Hi,

you should read lambda directly.
ip_data should refer to the internal representation of your problem, 
where inequality constraints have been reformulated to equality 
constraints (+ slack variables), so it might not be obvious what y_c() 
or y_d() mean.
You can have a look at the documentation of intermediate_callback to get 
some idea how to bring y_c() and y_d() into the TNLP space:
http://www.coin-or.org/Ipopt/documentation/node23.html#SECTION00053410000000000000

Hope that helps,
Stefan

On 10/28/2016 12:44 AM, Chunhua Men wrote:
> Hello,
>
> I have a convex model with some inequality (<=)  nonlinear constraints. At
> the end of the optimization,  I wanted to get the Lagrange multipliers for
> each constraint. I found out there were 2 ways within
> "finalize_solution(...)" to get them:
> 1) read "lambda" directly;
> 2) get from ip_data->curr()->y_d(). And I had to do some data transfer to
> get it -  and what I did was "static_cast<const
> Ipopt::DenseVector*>(GetRawPtr(ip_data->curr()->y_c()))".
>
> However, these 2 methods could not give me the completely same results. In
> my case, there were 6 constraints: 3 of them were the same, and 3 of them
> were not.
>
> Did I do anything wrong? and what is the best way to get Lagrange
> multipliers?  BTW, I am using " limited-memory" as "hessian_approximation",
> so I could not get Lagrange multipliers from "eval_h".
>
> Thanks! Chunhua
>
>
>
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-- 
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