Thanks Martin. That is exactly what I want. Feng
On Sat, Jan 29, 2011 at 11:59 PM, Martin Maechler < maech...@stat.math.ethz.ch> wrote: > >>>>> "FL" == Feng Li <m...@feng.li> > >>>>> on Sat, 29 Jan 2011 19:46:48 +0100 writes: > > FL> I meant "sparse matrix", sorry for the typo. > aha.. :-) > > > FL> On Sat, Jan 29, 2011 at 7:02 PM, Feng Li <m...@feng.li> wrote: > > >> Dear R, > >> > >> I have a simple question concerning with a special case of > >> sparse matrix multiplications. Say A is a 200-by-10000 dense > >> matrix. B is a 10000-by-10000 block- diagonal matrix, and each > >> diagonal block B_i is 100-by-100. The usual way I did A%*%B > >> will take about 30 seconds which is to time consuming because I > >> have to do this thousands of times. I also tried to partition A > >> into 100 small blocks and use mapply function to multiply by > >> each B_i, but that is even slower. > >> > >> I am wondering if there is an efficient way to perform this > >> type of multiplication with R? > > yes: e.g., via the (recommended, i.e. already installed) Matrix > package's bdiag() : > > require(Matrix) > set.seed(1) > A <- matrix(rnorm(2e6), 200, 10000) > Blis <- lapply(1:100, function(i)matrix(rnorm(1e4), 100,100)) > > system.time(B. <- .bdiag(Blis)) # 1.28 sec > system.time(cc <- A %*% B.) # 1.7 sec > class(cc)# "dgeMatrix" .. i.e. dense > ## and depending on the context you may revert to traditional unclassed > matrices > ## via > c2 <- as(cc, "matrix") > > > >> Thanks in advance! > > you are welcome. > Martin Maechler, ETH Zurich > > > >> Feng > >> > >> -- > >> Feng Li Department of Statistics Stockholm University 106 91 > >> Stockholm, Sweden http://feng.li/ > -- Feng Li Department of Statistics Stockholm University 106 91 Stockholm, Sweden http://feng.li/ [[alternative HTML version deleted]] ______________________________________________ R-help@r-project.org mailing list 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.
