[Ipopt] solving non-smooth large scale problem using ipopt

[Sebastian Nowozin]

Hi Chivalry,

convex + non-smooth problems often originate from minimizing the maximum of
multiple smooth convex problems.

If this is the case in your instance, you can reformulate the problem by
introducing additional variables containing the individual objective
function values, and a set of inequality constraints.
The resulting instance is a smooth convex problem with convex constraints
and as such amenable using IpOpt.

Sebastian

On Fri, May 8, 2015 at 3:45 PM, key01023 at gmail.com <key01023 at gmail.com>
wrote:

> Dear all,
> I have a large class of non smooth convex optimization problems with about
> 8000 dimensions and I am looking for c++ solvers to solve that. I tried
> level method  (extremely slow due to built up of sub-gradients), optimal
> gradient method due to Nesterov (very slow convergence), limited memory
> bundle method (LMBM) (no compilable solver available, failed to compile
> LMBM). I want to ask whether ipopt can be applied to solve this problem,
> because it uses limited memory BFGS which i guess somewhat resembles the
> LMBM methods. If so, is there some c++ file examples showing a case where
> we don’t need to provide the Hessian. Thank you.
>
> With regards,
> Chivalry
>
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>
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