similar to: Translation of Linear minimization probelm from matlab to r

Dear R team! =20 I found in HTML help for function solveLP(linprog) a small mistake. It says in Description "Minimizes c'x, subject to A x >=3D b and x >=3D 0", but tests show that there should be A x <=3D b. =20 Best regards, =20 Ludek =20 =20 [[alternative HTML version deleted]]

Full_Name: wfeng Version: 2.7 OS: windows xp Submission from: (NULL) (208.62.252.2) for solveLP(linprog), the program is specified as Minimizes c'x, subject to A x >= b and x >= 0. However, what I found is the actual constraints that works with the function are A x <= b and x >= 0.

Dear all is there any linear programming routine available for R? if not, can you suggest any alternatives? not need to be very powerful, I get only a samll problem to resolve. many thanks yong

Greetings I'm a total newbie in R and I'm trying to make a comparisson of Excel and R in the fields of: - optimisation modeling (using solver) - decision trees - simulation modeling as described in Winston, Wayne L.: Practical Management Science. for optimisation modeling in Excel I would normaly use solver. In R however I can't seem to be able to find the solution. I've

Hi. I am aware of three different R packages for linear programming: glpk, linprog, lpSolve. From what I can tell, if there are N variables and M constraints, all these solvers require the full NxM constraint matrix. Some linear solvers I know of (not in R) have a sparse matrix input format. Are there any linear solvers in R that have a sparse matrix input format? (including the

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Hi, I am using R in one of my courses. I am trying to use R's linprog package to solve to formulate 2-class classification problem as Linear programming problem. For my formulation, I need to set to "cvec" to all 0s. I know the points are linearly separable so an optimal solution "x" does exist, which satisfies all the constraints. But given the constraints and setting

Dear all, I am trying to find a solution satisfying the below equations in R. Set up the problem 9 X1+ X2 + X3 = 2 X1+ X2 + X3 = 1 which is subjected to 0 < X1 < X2 < X3 < 2. I have downloaded the packages \'linprog\' and \'lpSolve\' but can not see how to solve the question. Thank you for your help. With

Hi list, I want to find the total maximum resources I can spend given a set allocation proportion and some simple budget constraints. However, I get suboptimal results via lp and friends (i.e. lpSolve and simplex in the linprog and boot) . For example: library(lpSolve) proportions = c( 0.46, 0.28, 0.26) constraints = c( 352, 75, 171) lp(objective.in = proportions, const.mat =

Hello, I am trying to get a package to pass R CMD check on an iMac running Mac OS X. When the package is named safeBinaryRegression I get the following warning from R CMD check: * checking whether the name space can be loaded with stated dependencies ... WARNING Error in dyn.load(file, DLLpath = DLLpath, ...) : function 'make_lp' not provided by package 'lpSolveAPI'

@Don your solution does not solve Vijayan's scenario 2. I used spread and gather for that. An alternative solution to insert mising Fval - picking up with Don's newtst - is newtst <- c("FName: fname1", "Fval: Fval1.name1", "FName: fname2", "Fval: Fval2.name2", "FName: fname3", "FName: fname4", "Fval: fval4.fname4")

Using Ulrik?s example data (and assuming I understand what is wanted), here is what I would do: ex.dat <- c("FName: fname1", "Fval: Fval1.name1", "Fval: ", "FName: fname2", "Fval: Fval2.name2", "FName: fname3") tst <- data.frame(x = ex.dat, stringsAsFactors=FALSE) sp <- strsplit(tst$x, ':', fixed=TRUE) chk <-

Hi, I'm an R newbie and I've been struggling with a optimization problem for the past couple of days now. Here's the problem - I have a matrix of expected payouts from different stock option strategies. Each column in my matrix represents a different stock and each row represents the return to the strategy given a certain market move. So the rows are not a time series of percentage

Hi Vijayan, one way going about it *could* be this: library(dplyr) library(tidyr) library(purrr) ex_dat <- c("FName: fname1", "Fval: Fval1.name1", "Fval: ", "FName: fname2", "Fval: Fval2.name2", "FName: fname3") data.frame(x = ex_dat) %>% separate(x, c("F1", "F2"), sep = ": ") %>% filter(F2

Hi r-users,   I have the following code to solve 4 simultaneous eqns with 4 unknowns using newton iteration method.  But I got the error message:   pars <- c(1.15, 40, 50, 0.78) newton.input2 <- function(pars) {  ## parameters to estimate      alp <- pars[1]    b1  <- pars[2]     b2  <- pars[3]    rho <- pars[4]   f1 <- pars[1]*pars[2] f2 <-

I am attempting to lower the selectCC instruction to the instruction set of the backend I'm working on and I cannot seem to find a way to correctly implement this instruction. I know how this instruction should get implemented; I just have yet to find a way to do it. I want the select_cc instruction to be lowered into a comparison followed by a conditional move. I've attempted to use a

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) }

Hello All, Could anybody point me out any port of "Global Optimization Test Problems" OR "Constraint Satisfaction Test Problems" part of Coconut[%] benchmark or other such similar benchmarks for R. [%] Coconut benchmark: http://www.mat.univie.ac.at/users/neum/public_html/glopt/coconut/Benchmark/Benchmark.html Cheers! Isa [[alternative HTML version

Hi R-list, I am new to optimization in R and would appreciate help on the following question. I would like to minimize the following function using two constraints: ###### fn <- function(par,H,F){ fval <- 0.5 * t(par) %*% H %*% par + F%*% par fval } # matrix H is (n by k) # matrix F is (n by 1) # par is a (n by 1) set of weights # I need two constraints: # 1.

Hi, I'm trying to figure out how anova works in R by translating the examples in Sokal And Rohlf's (1995 3rd edition) Biometry. I've hit a snag with planned comparisons, their box 9.4 and section 9.6. It's a basic anova design: treatment <- factor(rep(c("control", "glucose", "fructose", "gluc+fruct",