Well, I finally figured out to do some algebra transformation and the problem was reduced to non-linear optimization on bounded areas. But on my way to this I ran into IMSL Fortran library function NNLPF. And its documentation toke me to DONLP2, a free Fortran package solving a huge family of nonlinear optimization problems.
However, I just had great difficulty understanding the comments and integrating them to form an R subroutine. Maybe someone can do this in the future. On Thu, Sep 11, 2008 at 10:32 AM, roger koenker <[EMAIL PROTECTED]> wrote: > I would be very wary of such approaches; my experience is that MM is > inferior > to the early affine-scaling versions of interior point algorithms for linear > programming > problems, and modern implementations like the Mehrotra version of the primal > dual > algorithm are much, much quicker and more reliable. More general convex > programming > is more delicate, and it is unlikely that methods that aren't that > successful with LPs > improve their performance in more complex settings. Something in R based on > CVX > or Saunder's PDCO, or similar would be very welcome. Meanwhile, as I've > said > earlier on R-help, it is fairly convenient to link these options to R via > R.matlab. > > 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 Sep 11, 2008, at 9:10 AM, Ravi Varadhan wrote: > >> >> Ken Lange's MM `algorithm' is a possibility for these non-smooth,, convex >> problems. It has been implemented in `constrOptim' for handling linear >> inequality constraints in the minimization of smooth objective functions. >> I >> have extended this to nonlinear inequalities. It can be further extended >> for convex functions, if one can come up with a smooth function that >> majorizes the convex objective function. This can be easily done for the >> absolute value function. >> >> Ravi. >> >> >> >> ---------------------------------------------------------------------------- >> ------- >> >> Ravi Varadhan, Ph.D. >> >> Assistant Professor, The Center on Aging and Health >> >> Division of Geriatric Medicine and Gerontology >> >> Johns Hopkins University >> >> Ph: (410) 502-2619 >> >> Fax: (410) 614-9625 >> >> Email: [EMAIL PROTECTED] >> >> Webpage: http://www.jhsph.edu/agingandhealth/People/Faculty/Varadhan.html >> >> >> >> >> ---------------------------------------------------------------------------- >> -------- >> >> >> -----Original Message----- >> From: [EMAIL PROTECTED] [mailto:[EMAIL PROTECTED] >> On >> Behalf Of Hans W. Borchers >> Sent: Thursday, September 11, 2008 7:19 AM >> To: [EMAIL PROTECTED] >> Subject: Re: [R] Convex optimization in R? >> >> Hesen Peng <hesen.peng <at> gmail.com> writes: >> >>> >>> Hi my R buddies, >>> >>> I'm trying to solve a specific group of convex optimization in R. The >>> admissible region is the inside and surface of a multi-dimensional >>> eclipse area and the goal function is the sum of absolution values of >>> the variables. Could any one please tell me whether there's a package >>> in R to do this? Thank you very much, >> >> >> To my knowledge there does not exist a designated R package for convex >> optimization. Also, in the Optimization task view the AMS nomenclature >> 90C25 for "Convex programming" (CP) is not mentioned. >> >> On the other hand, this task view may give you some ideas on how to solve >> your problem with one of the available optimization packages. >> For instance, a problem including sums of absolute values can be modeled >> as >> a linear program with mixed integer variables (MILP). >> >> There is a free module for 'disciplined' convex optimization, CVX, that >> can >> be integrated with Matlab or Python. Hopefully, there will be a CVX R >> package in the future (as has been announced/promised). >> >> Hans Werner Borchers >> ABB Corporate Research >> >> >>> Best wishes, >>> >>> -- >>> Hesen Peng >>> http://hesen.peng.googlepages.com/ >> >> ______________________________________________ >> 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. >> >> ______________________________________________ >> 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. > > ______________________________________________ > 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. > -- 彭河森 Hesen Peng http://hesen.peng.googlepages.com/ ______________________________________________ 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.