similar to: Simulated annealing using optim()

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 function

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

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

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 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 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

Can anyone help me with lbfgsb (function for optimization)? It takes the following parameters: void lbfgsb (int n, int lmm, double *x, double *lower, double *upper, int *nbd, double *Fmin, optimfn fn, optimgr gr, int *fail, void *ex, double factr, double pgtol, int *fncount, int *grcount, int maxit, char *msg, int trace, int nREPORT); What do I put for parameter ex (11th parameter)? I looked at

Hi there, I am trying to plot some output from a FORTRAN (ifort) program using R (2.5.1) under batch mode. In the FORTRAN code, I call R in batch mode to execute a script called fig1.R using something like PROGRAM test USE IFPORT IMPLICIT NONE DO !Some function which makes an output file called ~/fortran_output.txt CALL myfunc() !System call to R plotting routine CALL SYSTEM

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

Hi Stefanos, Attached the testcase. I tried to reduce it further, but the problem goes away when I remove the instructions further. There is a nested loop and the fault occurs while processing the inner loop (for.body) To reproduce the crash: opt -O3 testcase.ll -o out.ll > `groupChecks()` will only try to group pointers that are on the same alias set. If that’s true, the RT check

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

Hello, I Have a question related to the RT check on pointers during Loop Access Analysis pass. There is a testcase with loop code that consist of 4 different memory operations referring two global objects of different address spaces. One from global constant (address space 4, addr_size = 64) and the other from local, LDS (address space 3, addr_size= 32). (Details of various address spaces

I want to get some "simple" logic regression examples to work before exploring a hard problem. I can get results, but I'm having some problems using "cat" to save the logic regression equation to a disk file. Consider this: # Simple Logic Regression Example # efg, 23 Feb 2005 library(LogicReg) # Create simulated data with known logic equation: # "noise"

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

sorry to bother everyone. i have question to get the coefficient and confidence interval in Logic Regression with Logistic model. below i list the R code X <- matrix(as.numeric(runif(400) < 0.5), 50,8) 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

Dear R .Call() insiders, Can someone enlighten me how to properly finalize external pointers in C code (R-2.5.1 win)? What is the relation between R_ClearExternalPtr and the finalizer set in R_RegisterCFinalizer? I succeeded registering a finalizer that works when an R object containing an external pointer is garbage collected. However, I have some difficulties figuring out how to do that in an

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

A new version (0.7.1) of package 'subselect' has been uploaded to CRAN. Package 'subselect' provides functions which assess the quality of variable subsets as surrogates for a full data set, in an exploratory data analysis, and search for subsets which are optimal under various criteria. As of version 0.7 a new function 'leaps' has been added. 'Leaps' performs a