search for: annealing

...ct-Ex.R ... OK * checking examples ... ERROR Running examples in 'subselect-Ex.R' failed. The error most likely occurred in: > ### * anneal > > flush(stderr()); flush(stdout()) > > ### Name: anneal > ### Title: Simulated Annealing Search for an optimal k-variable subset > ### Aliases: anneal > ### Keywords: manip > > ### ** Examples > > > ## -------------------------------------------------------------------- > > ## > ## (1) For illustr...

Trying to install subselect v0.8 on Redhat 7.3 and R 1.8.1 fails (below). Any help is greatly appreciated. Xiao-Jun * Installing *source* package 'subselect' ... ** libs f2c < anneal.f > anneal.c anneal: Error on line 263: Declaration error for fica: adjustable dimension on non-argument Error on line 263: Declaration error for valp: adjustable dimension on non-argument Error

I'm preparing some code to compute the optimal geometry of stressed solids. The core of the calculations is the optimization of elastic energy using the simulated annealing method implemented in the R optim() rutine. I've defined a function to compute this "energy" scalar (the fn parameter for optim) and prepared a list with the arrays defining the geometry and the elastic properties of the solid (par parameter to optim). Finally, I tried to write my &q...

...aiting for the loop to finish, and my plot still disappears. Is there any equivalent to -persist in GNUPLOT to allow the x11 window to stay open after R exits, so I can examine the results while the program continues? To put all this in context, I want to examine the results of a simulated annealing process at each temperature reduction stage of the annealing schedule. I am not doing the annealing in R for reasons of speed: I only want to use R as a graphics engine. Many thanks Luke Spadavecchia

Hi I am looking to perform a discrete mean-variance optimisation, specifically to maximise the ratio of portfolio mean over portfolio standard deviation for a portfolio of several hundred stocks through discrete position size holdings in each stock, where all position sizes must be elements of a small finite set of integer amounts which include zero. I don't think any of the standard R

Hi I am looking to perform a discrete mean-variance optimisation, specifically to maximise the ratio of portfolio mean over portfolio standard deviation for a portfolio of several hundred stocks through discrete position size holdings in each stock, where all position sizes must be elements of a small finite set of integer amounts which include zero. I don't think any of the standard R

Hi, we have some code that does variable selection with a genetic algorithm or simulated annealing, using a linear regression routine or neural network as the objective function. This code is a mixture of fortran and C. The code is more than 15 years old and I am planning to rework it. Though a C rewrite would be good for efficiency, I would like to protoype it quickly in R and see how it perf...

Hello R-Help, I'm using R to do some optimization, specifically using the optim method with method = 'SANN' (simulated annealing). I read the help file, and noticed that this method does not include restarts/reheats, which I think would help my optimization significantly. Does anyone know of an implementation that does this? I searched both the internet and the help archives and was unable to find anything. Thanks in adv...

Dear list, I was trying to use the R API for optimization method "Simulated annealing" void samin(int n, double *x, double *Fmin, optimfn fn, int maxit, int tmax, double temp, int trace, void *ex); but I encountered the following problem: The implementation of the function samin (as seen in src/main/optim.c) passes its void * argument "ex" into the...

...ot;") rownames(X) <- paste("case", 1:nrow(X), sep="") # Define expected result: Y = (NOT X2) AND X6 Y <- as.numeric(!X[,2] & X[,6]) # set seed for reproducible test set.seed(19937) # 100 interations too few: some results in single node with |Parameter| < 1 Annealing <- logreg.anneal.control(start = -1, end = -4, iter = 500, update = 50) logicfit <- logreg(resp=Y, bin=X, type = REGRESSION.TYPE<-2, select = FIT.SINGLE.MODEL<-1, ntrees=1, nleaves=2, # force shape of final t...

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...

1. Most of the neighbors of a low cost state, x, may have much higher costs; and 2. The problem is highly degenerate in the sense that there are states, x, with a large (sub) neighborhood of equal cost states, S(x) = fy inside N(x) and f(y) = f(x). In this case, even rejecting all the proposals that would take us out of S, would still give us a significant acceptance rate. The best way we found

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

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

...ng 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 NULL when we treat "method" as "SANN". But in "constrOptim", the function does not allow us to set gradient as "SANN" if we set "method" to be "SANN". I am just curious...

...8) colnames(X) <- paste("X", 1:ncol(X), sep="") rownames(X) <- paste("case", 1:nrow(X), sep="") # Define expected result: Y = (NOT X1) AND X5 Y <- as.numeric(!X[,1] & X[,5]) z<-as.numeric(runif(50)) model.dat<-cbind(X,z,Y) set.seed(12345) Annealing <- logreg.anneal.control(start = 4, end = -4, iter = 1000, update = 50) logicfit <- logreg(resp=model.dat[,10], bin=model.dat[,1:8],sep=model.dat[,9], type = 3, select = 2, ntrees=2, nleaves=3, anne...

I am writing code in C and would like to call R's functions samin and vmmin (optimization routines: simulated annealing and BFGS) I do not understand how to create and pass in the function (as well as the extra arguments it needs) I am optimizing. I have read the R Extensions manual but it is still unclear to me. Could you give me some pointers and/or direct me to some example code which calls one of the optimizer...

...subselect, the quality of given k-subsets of variables are assessed under three criteria (Reference 2). Three additional functions, 'anneal', 'genetic' and 'improve', search for optimal k-variable subsets under those criteria, using three different algorithms: a simulated annealing algorithm, a genetic algorithm and a restricted local improvement algorithm (Reference 1). Among the options, the user can control number of iterations, initial temperature, cooling factors and cooling frequency in simulated annealing, and number of generations, population size, admissibility of cl...

...subselect, the quality of given k-subsets of variables are assessed under three criteria (Reference 2). Three additional functions, 'anneal', 'genetic' and 'improve', search for optimal k-variable subsets under those criteria, using three different algorithms: a simulated annealing algorithm, a genetic algorithm and a restricted local improvement algorithm (Reference 1). Among the options, the user can control number of iterations, initial temperature, cooling factors and cooling frequency in simulated annealing, and number of generations, population size, admissibility of cl...

Hello list, I am dealing with a noisy function (gradient,hessian not available) with simple boundary constraints (x_i>0). I've tried constrOptim() using nelder mead to minimize it but it is way too slow and the returned results are not satisfying. simulated annealing is so hard to tune and it always crashes R program in my case. I wonder if there are any packages or functions can do direct search optimization? A rough search in literature shows multidirectional search and DIRECT algorithm may help. Is there any other satisfying algorithm? Thanks, WC