similar to: A combinatorial optimization problem: finding the best permutation of a complex vector

Dear R-experts, this is not a direct R-problem but I hope you can help me anyway. I would like to minimize || PG-I || over P, where P is a p x p permutation matrix (obtained by permuting the rows and/or columns of the identity matrix), G is a given p x p matrix with full rank and I the identity matrix. ||.|| is the frobenius norm. Does anyone know an algorithm to solve such a problem? And if

Hello, 1) Is there any nonlinear programming optmizer that I can user for the following problem? Obj function: (max) revenue = price * volume Constraints: price and volume pair must be from the following variable data set: Variable data set: # price volume 1 10 500 2 20 450 3 30 330 4 40 250 5 50 190 Expected result: 10,000 (variable row#4) 2) Could it also be possible for the suggested

For an application in image processing -- using R for statistical purposes -- I need to solve the following task: Given n (e.g. n = 100 or 200) points in the unit square, more or less randomly distributed. Find a rectangle of maximal area within the square that does not contain any of these points in its interior. If a, b are height and width of the rectangel, other constraints may have to be

Hello I was trying to estimate the weibull model using nls after putting OLS values as the initial inputs to NLS. I tried multiple times but still i m getting the same error of Error in nlsModel(formula, mf, start, wts) : singular gradient matrix at initial parameter estimates. The Program is as below > vel <- c(1,2,3,4,5,6,7,8,9,10,11,12,13,14) > df <- data.frame(conc, vel) >

Hi, so I don't speak computer and I have no idea what this code is telling the program to do, but I apparently need to be able to find and isolate influencial observations. Problem, I have no idea what the error means and where it may be from in the code. error I get is below the code { ## OLS results NameC<- lm(gpanew~female+female:lastinit+agenew+canadian+mom_ed+yearstudy) ## default:

Dear List-members, I found a strange behaviour in the lqs function. Suppose I have the following data: y <- c(7.6, 7.7, 4.3, 5.9, 5.0, 6.5, 8.3, 8.2, 13.2, 12.6, 10.4, 10.8, 13.1, 12.3, 10.4, 10.5, 7.7, 9.5, 12.0, 12.6, 13.6, 14.1, 13.5, 11.5, 12.0, 13.0, 14.1, 15.1) x1 <- c(8.2, 7.6,, 4.6, 4.3, 5.9, 5.0, 6.5, 8.3, 10.1, 13.2, 12.6, 10.4, 10.8, 13.1, 13.3, 10.4, 10.5, 7.7, 10.0, 12.0,

I am new to R and Im some trouble with the following question... Generate 100 standard normal N(0,1) samples of size 100, X1(k),...,X100(k) where k=1,...,100 (The k is and indicie in brackets) Calculate the sample mean for each sample. For each sample mean Xbark the 0.95-confidence interval for the mean mew=0 is given by... Ik= ( Xbark plus or minus 1.96/10) Find the number of intervals such

I'm trying to use least trimmed squares using ltsreg with nsamp="exact". When I use the following: rg <- ltsreg(x,y,nsamp="exact") I get: Error in lqs.default(x, y, nsamp = "exact", method = "lts") : NAs in foreign function call (arg 10) In addition: Warning message: NAs introduced by coercion Incidentally, there are no missings in x or y,

I have found that specifying different "sizes" in the sample command has a funny effect on the random sampling. The code below is a condensed version of a function I wrote to simulate a bootstrap method. For simplicity, I eliminated the internal bootstrap loop, but kept a statement to draw one bootstrap sample, because this is where the problem occurs. The output (mean(y)^2) should be

Hello, I've a problem with a self written routine taking a lot of memory (>1.2Gb). Maybe you can suggest some enhancements, I'm pretty sure that my implementation is not optimal ... I'm creating many linear models and store coefficients, anova p-values ... all I need in different lists which are then finally returned in a list (list of lists). The input is a matrix with 84 rows

Hello! I have the following combinatorial problem. Consider the cumulative sums of all permutations of a given weight vector 'w'. I need to know how often weight in a certain position brings the cumulative sums equal or above the given threshold 'q'. In other words, how often each weight is decisive in raising the cumulative sum above 'q'? Here is what I do: w <-

Dear All,   I have difficulty in installing the package of Rcplex. Its description web is at http://cran.r-project.org/web/packages/Rcplex/index.html   First, I tried -------------------------------------------------------------------------------------------------- url <- "http://cran.r-project.org/src/contrib/Rcplex_0.3-0.tar.gz"; download.file(url, basename(url),

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

Hi, I am writing a function to plot a pdf of a distribution, GNL.pdf.fn = function(x,mu,sigma,alpha,beta,rho) { y = x-rho*mu cf.fn = function(s){ cplex = complex(1,0,1) temp1 = alpha*beta*exp(-sigma*s^2/2) temp2 = (alpha-cplex*s)*(beta+cplex*s) out = (temp1/temp2)^rho out } temp.fn = function(s){ (Mod(cf.fn(s)))*cos(Arg(cf.fn(s))-s*y) } int.fn =

Hi, sample(x) only permutates and/or samples from a vector, which I can't use for a matrix. Please help. Thanks Stanley [[alternative HTML version deleted]]

All, In the function below I have 24 individuals and 6 calculations per individual. The 6 calculations are collected each time in a 1:24 loop when calculating "delta". I'd like to collect all 144 = 24*6 calculations in one vector ("delta.patient.comb"). The function works as is via indexing, but is there an easier way to collect the measurements via appendinng the 6

Preallocation Instruction Scheduling with Register Pressure Minimization Using a Combinatorial Optimization Approach G. Shobaki, M. Shawabkeh and N. Abu-Rmaileh ACM Transactions on Architecture and Code Optimization (TACO). vol. 10, issue 3, Article 14 (Sept. 2013) http://dx.doi.org/10.1145/2512432 Regards Ghassan Shobaki, PH.D Assistant Professor Department of Computer Science Princess Sumaya

my reproducible example test<-structure(list(site = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 5L), .Label = c("A", "B", "C", "D", "E"), class = "factor"),

Using the Cement hardening data in Anderson (2008) Model Based Inference in the Life Sciences. A Primer on Evidence, and working with the best model which is lm ( y ~ x1 + x2, data = cement ) the AIC value from R is model <- lm ( formula = y ~ x1 + x2 , data = cement ) AIC ( model ) 64.312 which can be converted to AICc by adding the bias

Hello, I am an R user who wants to solve an optimization problem. To do so, I tried to install the package Rcplex on my computer (Windows7-64), so I did the same as installation Instructions<http://www.geeklog.net/docs/english/install.html> and edited the file "Makevars.win" by adding the path "C:/Program Files/IBM/ILOG/CPLEX_Studio_Preview124/cplex/include" (where the