search for: sanne

I think SANN method in optim() is failing to report that it has not converged. Here is an example genrose.f<- function(x, gs=NULL){ # objective function ## One generalization of the Rosenbrock banana valley function (n parameters) n <- length(x) if(is.null(gs)) { gs=100.0 } fval<-1.0 + sum (gs*(x[1:(n-1)]^2 - x[2:n])^2 + (x[2:n] - 1)^2) return(fval) }

Hi Everyone, I'm trying to minimize a function using constrOptim with the simulated annealing method SANN. If I understand constrOptim well, it basically passes most of its arguments to optim while somehow enforcing the constraints. My problem is, that since SANN does not need gradients, when using optim with SANN, the gr argument of optim is used to specify a function to create the next

Dear R users, I am running a maximum likelihood model with optim. I chose the simulated annealing method (method="SANN"). SANN is not performing bad, but I guess it would be much more effecive if I could set the `parscale' parameter. The help sais: `parscale' A vector of scaling values for the parameters. Optimization is performed on `par/parscale' and these

Hi all I am trying to solve a combinatorial optimization problem. Basically, I can reduce my problem into the next problem: 1.- Given a NxN grid of points, with some values in each cell 2.- Find the combination of K points on the grid such that, the maximum mean value is obtained I took the Travel SalesMan problem example in ?optim documentation. I am not sure if I have understood correctly

Hello List I'm working on a combinatoric problem in which the object is to minimize the badness() of a vector. I think this class of problem is only soluble by optim() using method=SANN. The badness() of anything is >= 0, and when I've found a solution with zero badness, I want optim() to stop (carrying on beyond zero badness cannot improve the solution). Efficiency is crucial here.

Dear All, I am having trouble in using R function "constrOptim" to do constraint optimization. It seems that "constrOptim" calls function "optim" when it does the optimization, and "optim" allows us to set "method" to be "SANN" if we want to use simulated annealing. In "optim", the function allows us to set gradient to be

Hi Everyone, I'm trying to minimize a function using constrOptim with the simulated annealing method SANN. If I understand constrOptim well, it basically passes most of its arguments to optim while somehow enforcing the constraints. My problem is, that since SANN does not need gradients, when using optim with SANN, the gr argument of optim is used to specify a function to create the next

Hello, I wonder why the control parameter REPORT is not supported by method SANN. Looking into optim.c I found an internal constant: #define STEPS 100 ... and decreasing this to 10 helped me fine-tuning the annealing parameters in an actual problem. Is there any reason why not passing nREPORT to samin and setting something like: STEPS = nREPORT / tmax Thomas P. -- Thomas Petzoldt

I tried optim using the SANN algoithm. To start things out I tried the example of solving the "traveling salesman" problem as given in the documentation. The example works just fine. But if I comment out the line: set.seed(123) # chosen to get a good soln relatively quickly More often than not it doesn't converge to the optimum solution as shown in the example. Alos with trace on

I am trying to control the behavior of the SANN method in optim (R 2.14.1) via control$temp. In my toy tests it works; in my real use, it doesn't. As far as I can tell my code with different temp values is loaded; I even traced into the function that calls optim and verified temp had the value I had set. Could the fact that I have NaN's coming back from the objective function be a

I have a problem using optim, so I am hoping someone can help me out with it: Suppose I have the list: list(D,R,P) [[1]] V1 V2 V3 V4 1 0 1 0 1 2 1 1 0 0 3 1 0 1 0 4 0 0 1 1 5 0 1 0 1 6 1 1 0 0 7 1 0 1 0 8 0 0 1 1 9 1 0 1 0 10 0 0 1 1 [[2]] [1] 23 85 12 73 23 24 25 56 78 1200 [[3]] V1 1 25 2 80 3 15 4

...tics for all the LLVM tools. I intend to implement the necessary infrastructure and convert a (small) number of diagnostics. Further conversions can be done incrementally. Any comments on this approach? Is this the right direction for diagnostics in LLVM? Suggestions more than welcome! Thanks, Sanne -------------- next part -------------- An HTML attachment was scrubbed... URL: <http://lists.llvm.org/pipermail/llvm-dev/attachments/20170313/49b7bc0a/attachment.html>

Hello: I'm trying to use Rcmd SHLIB to compile a single file, sann.c, to get sann.dll. I was able to get make libR.a to work, after going into MkRules and changing the line DLLTOOL=$(BINPREF)dlltool -k --as $(AS) to read DLLTOOL=C:/MINGW-1.1/bin/dlltool -k --as $(AS) But now I get: C:\rw1051\src\gnuwin32>Rcmd SHLIB sann.c make: make: Command not found make: *** [libR] Error 127

Folks: I thought John Nash's comment below was profound and a possible Fortunes candidate: (Aside: I believe it applies to a great deal of what is discussed on this list, not just stochastic optimization.) Cheers, Bert ... (in the context of stochastic optimization) >... As with many tools in this domain, for effective use they > require more knowledge than many of their users

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

Hi R users, I am using the optim funciton to maximize a log likelihood function. My code is as follows: p<-optim(c(-0.2392925,0.4653128,-0.8332286, 0.0657, -0.0031, -0.00245, 3.366, 0.5885, -0.00008, 0.0786,-0.00292,-0.00081, 3.266, -0.3632, -0.000049, 0.1856, 0.00394, -0.00193, -0.889, 0.5379, -0.000063, 0.213, 0.00338, -0.00026, -0.8912, -0.3023, -0.000056), f,

Hello: I am having some difficulty with a DLL, using > version _ platform i386-pc-mingw32 arch x86 os Win32 system x86, Win32 status major 1 minor 2.1 year 2001 month 01 day 15 language R I have a library called

Hi, I'm having some difficulties formulating this question. But what I want, is to extract the options associated with a parameter for a function. e.g. method = c("Nelder-Mead", "BFGS", "CG", "L-BFGS-B", "SANN") in the optim function. So I would like to have a vector with c("Nelder-Mead", "BFGS", "CG",

...red in a pool. I''ve also checked that the PIFs are in the same order on both nodes (the reference mentions this). The MTU (1500) of brib0 differs from that of ib0 (2044), but changing this does not solve the problem. Any help is much appreciated. Thanks! Trygve -- HypoBytes Ltd. Trygve Sanne Hardersen Akersveien 24F 0177 Oslo Norway hypobytes.com +47 40 55 30 25 _______________________________________________ Xen-users mailing list Xen-users@lists.xensource.com http://lists.xensource.com/xen-users

Hi, I got the following problem. I am doing a maximum likelihood estimation for a Kalman Filter. For this purpose, I have to invert an error matrix Ffast of dimension "no. parameters X no.parameters". The usualy optim methods often find only local minima, so I decided to make the optimization using the SANN algorithm, which is very slow already. However, this becomes a real problem