Hello, I am attempting to solve the least squares problem Ax = b in R, where A and b are known and x is unknown. It is simple to solve for x using one of a variety of methods outlined here: http://cran.r-project.org/web/packages/Matrix/vignettes/Comparisons.pdf
As far as I can tell, none of these methods will solve for x when A, x, and b are constrained to be non-negative (x > 0). Other packages, such as nnls, can solve the non-negative least squares problem, but do not work with very large sparse matrices. The matrix A that I am using is 750,000 by 46,000 elements with 99% zeros, and matrix b is a dense 750,000 by 1 matrix. Does an R function exist for solving the non-negative least squares problem with a sparse matrix? Thanks!, Erik > sessionInfo() R version 2.13.0 (2011-04-13) Platform: x86_64-apple-darwin9.8.0/x86_64 (64-bit) locale: [1] en_US.UTF-8/en_US.UTF-8/C/C/en_US.UTF-8/en_US.UTF-8 attached base packages: [1] stats graphics grDevices utils datasets methods base other attached packages: [1] nnls_1.3 Matrix_0.999375-50 MASS_7.3-12 [4] lattice_0.19-23 loaded via a namespace (and not attached): [1] grid_2.13.0 tools_2.13.0 ______________________________________________ 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.
