[R-SIG-Finance] Best optimizer for large scale problems

[alexios ghalanos]

For quadratic problems you really should use quadprog.

You can also try the 'parma' package which provides a nice interface to 
frame your problem and constraints and then solve it using either an LP, 
QP, NLP (with analytic derivatives) or GNLP formulation (depending on 
the intersection of problem and constraint type).

Regards,

Alexios

On 06/06/2013 08:58, Dominykas Grigonis wrote:
> ROI::ROI_solve( problem, solver, control, ... )
> DEoptim::DEoptim() # differential evolution!
>
> Rsolnp # general non-linear optimisation
>
> This is from my R conspects. Can try to take a look at those.
> For your problem quadprog might be the best option regarding speed. I like DEoptim a lot, but I use it for more complicated problems when I can not find simpler solution. Try adjusting parameters of it, it has 5 or 6 different approaches to the problem. Adding jitter helps in some cases(might slow process a bit, but not sure). I used it when I was optimising with respect to non-linear functions of portfolio evolution as drawdown.
>
>
>
> Kind regards,--
> Dominykas Grigonis
>
>
> On Thursday, 6 June 2013 at 07:55, Bastian Offermann wrote:
>
>> Hi all,
>> I am working on a large scale portfolio optimization problem with up to
>> 500 assets. My objective function is simple
>>
>> w*returns - 1/2 * 1/constant * w * Matrix * w
>>
>> subject to sum(w) == 1 and w is a vector of weights with 0 <= w[i] <= 1
>> for all i = 1, ..., n.
>>
>> I have tried quadprog, alabama and DEoptim. What are your experiences
>> with those and possibly other options?
>> Thanks in advance!
>>
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