Florin, Please allow me to clarify some issues:
Many, if not most, problems in science involve optimization in one form or another. Consequently, "optimization" is a vast area. There are many different types of optimization. Here is a one way to classify optimzation problems (neither mutually exclusive nor exhaustive): - smooth versus non-smooth - unconstrained versus constrained - linear versus non-linear - real & continuous versus discrete, integer or mixed (or combinatorial problems) - scalar versus multi-objective - small versus large-scale Given such vastness and diversity of optimization problems, it is important that one chooses an appropriate optimization tool for one's particular problem. I am not sure what kind of optimization problem that you were trying to solve, but the packages that you mentioned can only deal with real, smooth, box-constrained optimization (except for optim(), whose Nelder-Mead and SANN can handle real, non-smooth problems, but with no constraints). Large-scale discrete problems, such as binning problems or travelling salesman problem are challenging, and, as far as I know, R does not have strong capabilties in this area. 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 Florin Maican Sent: Thursday, April 02, 2009 11:33 AM To: [email protected] Subject: Re: [R] Constrined dependent optimization. I tried many optimizers in R on my large scale optimization problems. I am not satisfied with their speed on large op problems. But you may try in this order nlminb ucminf ucminf package spq BB package optim Is here someone that try to port Ipopt in R? https://projects.coin-or.org/Ipopt Florin On Thu, 2 Apr 2009 7:49:45 -0700 <[email protected]> wrote: > Sorry I sent a description of the function I was trying to minimize > but I must not have sent it to this group (and you). Hopefully with > this clearer description of my problem you might have some > suggestions. > > It is basically a warehouse placement problem. You have a warehouse > that has many items each placed in a certain bin (the "real" > warehouse has about 20,000 of these bins, hence the large number of > variables that I want to input to optimize). Now assume that an order > comes in for three items A, B, and C. In the worst case A will be on > one end of the warehouse, B in the middle and C on the other end of > the warehouse. The "work" involved in getting these items to fulfill > this order is roughly proportional to the distance from A to B plus > the distance from B to C (assuming the absolute positions are sorted). > So the cost for fulfilling this order is this distance. In the ideal > world A, B, and C would be right next to each other and the > cost/distance would be minimized. So the function I want to minimize > would be placing these 20,000 items in such a way so that the minimum > "work" is involved in fulfilling the orders for the past month or two. > Clearer? > > I can see that I may need to cut back on the variables 20,000 is > probably too many. Maybe I can take the top 1,000 or so. I just am not > sure of the packages available what to reasonably expect. I would like > this optimization to complete in a reasonable amount of time (less > than a few days). I have heard that SANN is slower than other > optimization methods but it does have the feature of supplying a > "gradient" as you pointed out. Are there other packages out there that > might be better suited to such a large scale optimizaiton? > > Thanks again. > > Kevin > ---- Paul Smith <[email protected]> wrote: > > As I told you before, without knowing the definition of your > > function f, one cannot help much. > > > > Paul > > > > > > On Wed, Apr 1, 2009 at 3:15 PM, <[email protected]> wrote: > > > Thank you I had not considered using "gradient" in this fashion. > > > Now as an add on question. You (an others) have suggested using > > > SANN. Does your answer change if instead of 100 "variables" or > > > bins there are 20,000? From the documentation L-BFGS-B is designed > > > for a large number of variables. But maybe SANN can handle this as > > > well. > > > > > > Kevin > > > > > > ---- Paul Smith <[email protected]> wrote: > > >> Apparently, the convergence is faster if one uses this new swap > > >> function: > > >> > > >> swapfun <- function(x,N=100) { > > >> loc <- > > >> c(sample(1:(N/2),size=1,replace=FALSE),sample((N/2):100,1)) tmp > > >> <- x[loc[1]] x[loc[1]] <- x[loc[2]] > > >> x[loc[2]] <- tmp > > >> x > > >> } > > >> > > >> It seems that within 20 millions of iterations, one gets the > > >> exact optimal solution, which does not take too long. > > >> > > >> Paul > > >> > > >> > > >> On Mon, Mar 30, 2009 at 5:11 PM, Paul Smith <[email protected]> > > >> wrote: > > >> > Optim with SANN also solves your example: > > >> > > > >> > ------------------------------------------- > > >> > > > >> > f <- function(x) sum(c(1:50,50:1)*x) > > >> > > > >> > swapfun <- function(x,N=100) { > > >> > loc <- sample(N,size=2,replace=FALSE) > > >> > tmp <- x[loc[1]] > > >> > x[loc[1]] <- x[loc[2]] > > >> > x[loc[2]] <- tmp > > >> > x > > >> > } > > >> > > > >> > N <- 100 > > >> > > > >> > opt1 <- > > >> > optim(fn=f,par=sample(1:N,N),gr=swapfun,method="SANN",control=l > > >> > ist(maxit=50000,fnscale=-1,trace=10)) > > >> > opt1$par opt1$value > > >> > > > >> > ------------------------------------------- > > >> > > > >> > We need to specify a large number of iterations to get the > > >> > optimal solution. The objective function at the optimum is > > >> > 170425, and one gets a close value with optim and SANN. > > >> > > > >> > Paul > > >> > > > >> > > > >> > On Mon, Mar 30, 2009 at 2:22 PM, Hans W. Borchers > > >> > <[email protected]> wrote: > > >> >> > > >> >> Image you want to minimize the following linear function > > >> >> > > >> >> f <- function(x) sum( c(1:50, 50:1) * x / (50*51) ) > > >> >> > > >> >> on the set of all permutations of the numbers 1,..., 100. > > >> >> > > >> >> I wonder how will you do that with lpSolve? I would simply > > >> >> order the coefficients and then sort the numbers 1,...,100 > > >> >> accordingly. > > >> >> > > >> >> I am also wondering how optim with "SANN" could be applied > > >> >> here. > > >> >> > > >> >> As this is a problem in the area of discrete optimization > > >> >> resp. constraint programming, I propose to use an appropriate > > >> >> program here such as the free software Bprolog. I would be > > >> >> interested to learn what others propose. > > >> >> > > >> >> Of course, if we don't know anything about the function f then > > >> >> it amounts to an exhaustive search on the 100! permutations -- > > >> >> probably not a feasible job. > > >> >> > > >> >> Regards, Hans Werner > > >> >> > > >> >> > > >> >> > > >> >> Paul Smith wrote: > > >> >>> > > >> >>> On Sun, Mar 29, 2009 at 9:45 PM, <[email protected]> > > >> >>> wrote: > > >> >>>> I have an optimization question that I was hoping to get > > >> >>>> some suggestions on how best to go about sovling it. I would > > >> >>>> think there is probably a package that addresses this > > >> >>>> problem. > > >> >>>> > > >> >>>> This is an ordering optimzation problem. Best to describe it > > >> >>>> with a simple example. Say I have 100 "bins" each with a > > >> >>>> ball in it numbered from 1 to 100. Each bin can only hold > > >> >>>> one ball. This optimization is that I have a function 'f' > > >> >>>> that this array of bins and returns a number. The number > > >> >>>> returned from f(1,2,3,4....) would return a different number > > >> >>>> from that of f(2,1,3,4....). The optimization is finding the > > >> >>>> optimum order of these balls so as to produce a minimum > > >> >>>> value from 'f'.I cannot use the regular 'optim' > > >> >>>> algorithms because a) the values are discrete, and b) the > > >> >>>> values are dependent ie. when the "variable" representing > > >> >>>> the bin location is changed (in this example a new ball is > > >> >>>> put there) the existing ball will need to be moved to > > >> >>>> another bin (probably swapping positions), and c) each > > >> >>>> "variable" is constrained, in the example above the only > > >> >>>> allowable values are integers from 1-100. So the problem > > >> >>>> becomes finding the optimum order of the "balls". > > >> >>>> > > >> >>>> Any suggestions? > > >> >>> > > >> >>> If your function f is linear, then you can use lpSolve. > > >> >>> > > >> >>> Paul > > >> >>> > > >> >>> ______________________________________________ > > >> >>> [email protected] 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. > > >> >>> > > >> >>> > > >> >> > > >> >> -- > > >> >> View this message in context: > > >> >> http://www.nabble.com/Constrined-dependent-optimization.-tp227 > > >> >> 72520p22782922.html Sent from the R help mailing list archive > > >> >> at Nabble.com. > > >> >> > > >> >> ______________________________________________ > > >> >> [email protected] 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. > > >> >> > > >> > > > >> > > >> ______________________________________________ > > >> [email protected] 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. > > > > > > > > > > ______________________________________________ > > [email protected] 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. > > ______________________________________________ > [email protected] 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. -- Florin G. Maican ================================== Ph.D. candidate, Department of Economics, School of Business, Economics and Law, Gothenburg University, Sweden ----------------------------------- P.O. Box 640 SE-405 30, Gothenburg, Sweden Mobil: +46 76 235 3039 Phone: +46 31 786 4866 Fax: +46 31 786 4154 Home Page: http://maicanfg.googlepages.com/index.html E-mail: [email protected] ------------------------------------ "Not everything that counts can be counted, and not everything that can be counted counts." --- Einstein --- ______________________________________________ [email protected] 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. ______________________________________________ [email protected] 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.
