>>>>> "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/ ______________________________________________ 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.