search for: mead

Hi, I have written an R translation of C.T. Kelley's Matlab version of the Nelder-Mead algorithm. This algorithm is discussed in detail in his book "Iterative methods for optimization" (SIAM 1999, Chapter 8). I have tested this relatively extensively on a number of smooth and non-smooth problems. It performs well, in general, and it almost always outperforms optim's...

Dear list,   I am looking for an R-only implementation of a Nelder-Mead process that can find local maxima of a spatially distributed variable, e.g. height, on a spatial grid, and outputs the coordinates of the new point during each evaluation. I have found two previous threads about this topic, and was wondering if something similar has been implemented since those me...

...CIFS connections from each filer are registered by the samba server and are logged in the file: 0.0.0.0.log Each of the filers moved to a new network. Filer #1 rejoined the domain but filer #2 can't. A tcpdump of the unsuccessful transaction is: 10:42:38.137963 IP gcc-fs1.netbios-ns > mead.netbios-ns: NBT UDP PACKET(137): MULTIHOMED REGISTRATION; REQUEST; UNICAST 10:42:38.138165 IP mead.netbios-ns > gcc-fs1.netbios-ns: NBT UDP PACKET(137): WACK; POSITIVE; RESPONSE; UNICAST 10:42:58.270693 IP mead.netbios-ns > gcc-fs1.netbios-ns: NBT UDP PACKET(137): REGISTRATION; NEGATIVE; RESP...

Hello, I have been using R to fit my data using non-linear least squares. I have used the optimize routine to minimize the sum of squared errors (using Nealder Meade optimization routine), but couldn't get the non-linear model in R to converge to the estimates acheived in a convergent Neadler Meade routine. It tells me about problems with the gradient. I was wondering if there is any way to estimate the covariance matrix for the parameters when using the...

Dear UseRs, Is there a way to define the lower-upper bounds for parameters fitted by optim using the Nelder-Mead method ? Thanks, Arnaud [[alternative HTML version deleted]]

Greetings! I want to follow the evolution of a Nelder-Mead function minimisation (a function of 2 variables). Hence each simplex will have 3 vertices. Therefore I would like to have a function which can output the coordinates of the 3 vertices after each new simplex is generated. However, there seems to be no way (which I can detect) of extracting this in...

...but it stopped regularly before to reach a true mimimum. It was not a problem with limit of iterations, just a local minimum. I was able sometimes to reach better minimum using several rounds of optim(). Then I moved to optimx() to do the different optim rounds automatically using "Nelder-Mead" and "BFGS" methods I find a huge time difference using system.time() based on the order of these both methods: > snb # "Nelder-Mead" and "BFGS" ??? user?? system? elapsed 1021.656??? 0.200 1021.695 > sbn # "BFGS" and "Nelder-Mead"...

All, In looking at `optim', it doesn't appear that it is possible to impose nonlinear constraints on Nelder- Mead. I am sufficiently motivated to try to code something in C from scratch and try to call it from R.... Does anyone have some good references to barrier and/or penalization methods for Nelder-Mead? I would ideally like some papers with pseudocode for method(s) that are in some sense optimal for c...

Dear all, I?m having some problems getting optim with method="Nelder-Mead" to work properly. It seems like there is no way of controlling the step size, and the step size seems to depend on the *difference* between the initial values, which makes no sense. Example: f=function(xy, mu1, mu2) { print(xy) dnorm(xy[1]-mu1)*dnorm(xy[2]-mu2) } f1=f...

...code works for some users, however my code crashes on the chol. Any suggestions? > mycop <- tCopula(param=0.5, dim=8, dispstr="ex", df=5) > x <- rcopula(mycop, 1000) > myfit <- fitCopula(x, mycop, c(0.6, 10), optim.control=list(trace=1), method="Nelder-Mead") Nelder-Mead direct search function minimizer function value for initial parameters = -1747.582044 Scaled convergence tolerance is 2.6041e-05 Stepsize computed as 1.000000 Error in chol(x, pivot = FALSE) : the leading minor of order 2 is not positive definite Kevin D. Oden e: k...

...nable, optimum, and seems to do so consistently --- i.e. it gets essentially the same estimates irrespective of starting values. We have plotted the log likelihood surface and it appears smooth and relatively innocuous. The phenomenon only occurs with the "L-BFGS-B"; the default (Nelder-Mead simplex) method, with a heavy penalty for violating constraints, seems to work just fine. So we can get solutions; it just makes me uneasy that there's this funny going on. Can anyone shed any light on what the problem is? I have enclosed below code to reproduce the phenomenon. It is probab...

...4) BBsolve(par = p0, fn = mgf_gammasum) dfsane(par = p0, fn = mgf_gammasum, control = list(trace = FALSE)) sane(par = p0, fn = mgf_gammasum, control = list(trace = FALSE)) and got the error message: > BBsolve(par = p0, fn = mgf_gammasum) Error in optim(par = par, fn = U, method = "Nelder-Mead", control = list(maxit = 100),  :   function cannot be evaluated at initial parameters Error in optim(par = par, fn = U, method = "Nelder-Mead", control = list(maxit = 100),  :   function cannot be evaluated at initial parameters Error in optim(par = par, fn = U, method = &quot...

...R/R1593_filtered.data",3.48)) + # sum(abs(localShifts))*lambda error <- sum(abs(localShifts))*lambda error # return the error to be minimized } Then I call optim: par <- seq(length=9, from=0, by=0) lambda <- 1/sqrt(147) optim(par, errorFunction, gr=NULL, method="Nelder-Mead", hessian=FALSE, globalShift, "/home/sarah/Semesterarbeit/Sequences/R/R1593_filtered.data", experimentalPI=3.48, lambda = lambda) The output is: $par [1] 0.56350964 0.56350964 0.56350964 0.56350964 0.00000000 -0.29515957 [7] 0.00569937 0.32543297 0.18...

...<- sum((LT - TAN(edad,f,c,a,d))^2) / N + logl <- (N/2)*log(sigma) + (sum((LT - TAN(edad,f,c,a,d))^2) / (2*sigma)) + } > ini.pars <- list(f=5.91e-05,c=-0.41732,a=0.009661,d=846.7179) > library(stats4) > erizo.mle <- mle(start= ini.pars, minuslogl = loglike, method="Nelder-Mead", control = list(maxit=1500, trace=TRUE)) Nelder-Mead direct search function minimizer function value for initial parameters = 1159.477620 Scaled convergence tolerance is 1.72776e-05 Stepsize computed as 84.671790 BUILD 5 3165.307359 1159.477620 . . . HI-REDUCTION 303 1158...

I was reading through the FlexMock docs and noticed the expectation method .mock, which returns the original mock associated with an expectation. It looks really handy for writing nice all-in-one mocks like: mock_user = mock(''User'').expects(:first_name).returns(''Jonah'').mock So I started playing around with mocha and found I could actually already do this!

...do functional outlier detection (using PCA to reduce to 2 dimensions - the functional boxplot methodology used in the Rainbow package), and using Hscv.diag function to calculate the bandwidth matrix where this line of code is run: result <- optim(diag(Hstart), scv.mat.temp, method = "Nelder-Mead", control = list(trace = as.numeric(verbose))) Within the optim function, there is a call to an external C function: .External2(C_optim, par, fn1, gr1, method, con, lower, upper) Where Par = (0.339, 0.339), fn1 = function (diagH) { H <- diag(diagH) %*% diag(diagH) if (default.bflag(d =...

Hi, Currently I am trying to solve a minimization problem using optim as method Nelder-Mead. However, Neldel-Mead needs many iterations until it finally converges. I have set $control.trace and $control.report such that I can see the value of the function at each iteration. I do see that I set the convergence criteria to strict in the sense that the function value does not change much. Ho...

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", "L-BFGS-B", "SANN") Or for instance the 'method' in the dist func...

...isy and smoothed noise functions we get for instance the following results: (Starting point is always `rep(0.1, 5)`, maximal number of iterations 5000, relative tolerance 1e-12, and the optimization is successful if the function value at the minimum is below 1e-06.) k nmk anms neldermead ucminf optim_BFGS --------------------------------------------------- 1 0.21 0.32 0.13 0.00 0.00 3 0.52 0.63 0.50 0.00 0.00 10 0.81 0.91 0.87 0.00 0.00 Solvers: nmk = dfoptim::nmk, anms = pracma::a...

Hi, am very new to R and I've written an optim function, but can't get it to work least.squares.fitter<-function(start.params,gr,low.constraints,high.constraints,model.one.stepper,data,scale,ploton=F) { result<-optim(par=start.params,method=c('Nelder-Mead'),fn=least.squares.fit,lower=low.constraints,upper=high.constraints,data=data,scale=scale,ploton=ploton) return(result) } least.squares.fitter(c(2,2),c(0,0),c(Inf,Inf),ricker.one.step...