On Sat, Mar 13, 2021 at 10:08 AM Spencer Graves <spencer.gra...@effectivedefense.org> wrote: > > TWO COMMENTS: > > > 1. DID YOU ASSIGN THE OUTPUT OF "optim" to an object, like "est <- > optim(...)"? If yes and if "optim" terminated normally, the 60,000+ > paramters should be there as est$par. See the documentation on "optim". > > > 2. WHAT PROBLEM ARE YOU TRYING TO SOLVE? > > > I hope you will forgive me for being blunt (or perhaps bigoted), but > I'm skeptical about anyone wanting to use optim to estimate 60,000+ > parameters. With a situation like that, I think you would be wise to > recast the problem as one in which those 60,000+ parameters are sampled > from some hyperdistribution characterized by a small number of > hyperparameters. Then write a model where your observations are sampled > from distribution(s) controlled by these random parameters. Then > multiply the likelihood of the observations by the likelihood of the > hyperdistribution and integrate out the 60,000+ parameters, leaving only > a small number hyperparameters.
Just a comment on this comment: I think it's perfectly reasonable to optimize 60k+ parameters with conjugate gradient. CG was originally developed to solve linear equations of the form Ax=b. If x was not large in size, one would just use solve(A, b) instead of an iterative method. Use of CG is quite common in image processing. A relatively small 300x300 image will give you 90k parameters. -Deepayan ______________________________________________ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
