Re: Large control loops

*To*: mathgroup at smc.vnet.net*Subject*: [mg122312] Re: Large control loops*From*: Ray Koopman <koopman at sfu.ca>*Date*: Mon, 24 Oct 2011 05:15:52 -0400 (EDT)*Delivered-to*: l-mathgroup@mail-archive0.wolfram.com*References*: <j80qff$ael$1@smc.vnet.net>

On Oct 23, 3:32 am, Arthur <shuk... at gmail.com> wrote: > Hi guys, > > I am trying to do the following (simplified somewhat) > > Create matrix A (250x2500 or so) > Run 2500 regressions (each column vs. a common vector) and collect > residuals > Use the residuals to estimate the parameters to 2500 stochastic > processes > Create a list with the top 20 said parameters > Multiply the result by matrix A > Collect results > > I can do all of this once, but I need to do all of it approximately > 1000 times with slightly different initial matrices. What do you > suggest the best way to go about it would be? If possible I would > like to not have to resort to a secondary language (C/C++, etc.) You haven't said what the elements of A and the common vector are. Are they all numeric? If so then make sure they're packed arrays. Also, you haven't said which direction the regressions are going. Is the common vector the predictor or the response? What kind of regressions? Ordinary least-squares linear, with an intercept? Or something more complicated? And how are you estimating the stochastic process parameters? Is this step likely to take most of the time? All I can suggest now is that things will probably go faster if you transpose A so that you work with rows instead of columns.