Re: [Quantlib-dev] global optimizers

[Dirk E. <ed...@de...>]

On 22 November 2013 at 21:58, Peter Caspers wrote:
| Hi,
| 
|    picking up the topic "global optimization" which was mentioned recently
| on the quantlib user conference I added an experimental simulated
| annealing optimizer here
| 
| https://github.com/lballabio/quantlib/pull/57
| 
| which is a reimplementation from the one in Numerical Recipes, 2nd ed.

Nice.
 
| I do not have real use cases at the moment. However I tried it with the
| toy example minimizing
| 
| f(x,y) = (2.0+sin(x)*sin(y))*(x*x+y*y)
| 
| starting at (10,20) with (a rather unfavorable) lambda = 1 for building
| the initial simplex. A start temperature of 1000.0 and 500 moves for
| cooling down to zero finds the global optimum at (0,0) in 667
| iterations. The success is obviously dependent on suitable parameters
| though.
| 
| DifferentialEvolution which is already available in the library does
| also find the minimum. It needs 5900 iterations in my experiment, but
| works out of the box with the standard configuration.

The bigger question is whether QL should venture into re-implementing topics
that are well-understood and provided by other domains?  

Eg R comes with five optimizers built in [as options to function optim()],
yet people keep contributing new ones:

   http://cran.r-project.org/web/views/Optimization.html

It's a tough call. There is definitely a use case for optimization, but maybe
a 'hook' (to the GSL, some coin-or algo, cplex, ... ) may be better in the
long run?

Dirk

-- 
Dirk Eddelbuettel | ed...@de... | http://dirk.eddelbuettel.com