If you can reformulate your LP as an L1 problem, which is known to be possible without loss of generality, but perhaps not without loss of sleep, then you could use the sparse quantile regression functions in the quantreg package.
url: www.econ.uiuc.edu/~roger Roger Koenker email [EMAIL PROTECTED] Department of Economics vox: 217-333-4558 University of Illinois fax: 217-244-6678 Champaign, IL 61820 On Mar 5, 2007, at 5:30 PM, Talbot Katz wrote: > Hi. > > I am aware of three different R packages for linear programming: glpk, > linprog, lpSolve. From what I can tell, if there are N variables > and M > constraints, all these solvers require the full NxM constraint > matrix. Some > linear solvers I know of (not in R) have a sparse matrix input > format. Are > there any linear solvers in R that have a sparse matrix input format? > (including the possibility of glpk, linprog, and lpSolve, in case I > might > have missed something in the documentation). Thanks! > > -- TMK -- > 212-460-5430 home > 917-656-5351 cell > > ______________________________________________ > R-help@stat.math.ethz.ch 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. ______________________________________________ R-help@stat.math.ethz.ch 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.
