Lui, you can also have a look at http://aqr.activequant.org/index.php/2010/08/genetically-optimizing-a-trading-system/for inspiration on how to genetically optimize a trading system, whether it's quadratic or not. It's just some snipplet but exemplifies it pretty well ...
Regards, Ulrich On Sun, Jan 23, 2011 at 11:15 PM, Guillaume Yziquel < [email protected]> wrote: > Le Sunday 23 Jan 2011 à 15:56:40 (-0600), Brian G. Peterson a écrit : > > On Saturday, January 22, 2011 04:33:09 pm Lui ## wrote: > > > Dear group, > > > > > > I was just wondering whether some of you have some experience with the > > > package "rgenoud" which does provide genetic algorithms for complex > > > optimization problems. > > > > <...> > > > > > What is your general experience? Did you ever try solving the > > > Markowitz portfolio with the rgenoud package? > > > I know that there are good solvers around for the qudratic programming > > > problem of the markowitz portfolio, but I want to go into a different > > > direction which translates into a quadratic problem with quadratic > > > constraints (and I havent found a good solver for that...). > > > > > > I am interested in your replies! Have a good weekend! > > > > As others have already said, for a quadtratic problem with quadratic > > constraints, there is an exact analytical solution. > > I wouldn't qualify dual interior point methods as an "exact" solution, but, > yes, that's the basic idea: they're better suited for that. > > > In these cases, you will be much better off both from a performance and > > accuracy perspective in using a quadratic solver (quadprog is most often > > applied in R, see list archives and many packages for examples). > > Is quadprog a second-order cone programming solver? If that is the case, > yes, it probably solves quadratic objective function with quadratic > constraints faster and with more accuracy than a full-fledged SDP > solver. > > > Other portfolio problems may be stated in terms of linear solvers, which > will > > likewise be faster than a global optimizer for finding an exact > analytical > > solution. > > > > If, however, your portfolio problem is non-convex and non-smooth, then a > > genetic algorithm, a migration algorithm, grid search, or random > portfolios > > may be a good option for finding a near-optimal portfolio. If this is > your > > true goal, perhaps you can say a little more about your actual > constraints and > > objectives (and use assets that are outside of your true area of > interest, > > such as the S&P sector indices). > > Yes, the problem structure often gives good insight as to which method > to apply. It may be noted, however, that quite a lot of non-convex > problems may be transformed into convex ones. And using some relaxation > methods, you can often use SDPs to optimise multivariate polynomial > objective under multivariate polynomial constraints, without too many > convexity hypothesis. > > SDPs are not always easy to manipulate, but they do solve a broad range > of optimisation problems. > > > Regards, > > > > - Brian > > Best regards, > > -- > Guillaume Yziquel > http://yziquel.homelinux.org > > _______________________________________________ > [email protected] mailing list > https://stat.ethz.ch/mailman/listinfo/r-sig-finance > -- Subscriber-posting only. If you want to post, subscribe first. > -- Also note that this is not the r-help list where general R questions > should go. > -- Ulrich Staudinger https://www.xing.com/profile/Ulrich_Staudinger [[alternative HTML version deleted]]
_______________________________________________ [email protected] mailing list https://stat.ethz.ch/mailman/listinfo/r-sig-finance -- Subscriber-posting only. If you want to post, subscribe first. -- Also note that this is not the r-help list where general R questions should go.
