similar to: lpSolve API - add Vs set

Dear R users, I would like to optimize a linear approximation of a quadratic function using lpSolve. My code runs without any error or warning message but the constraints that I set don't seem to work properly. Nevertheless, I am certain that my code is somewhere wrong. I would like to solve the following problem: max 2x-x^2+y subject to 2x^2 + 3y^2 <= 6 2>= x,y >= 0 I would

Dear all, I am interested in solving a MIP problem with binary outcomes and continuous variables, which ARE NOT RESTRICTED TO BE NEGATIVE. In particular, Max {z1,z2,z3,b1} z1 + z2 + z3 (s.t.) # 7 z1 + 0 z2 + 0 z3 + b1 <= 5 # 0 z1 + 8 z2 + 0 z3 - b1 <= 5 # 0 z1 + 0 z2 + 6 z3 + b1 <= 7 # z1, z2, z3 BINARY {0,1} # -5<= b1 <=5 (i.e. b1 <= 5; -b1 <= 5 ) Using

Hi. In the help page for "lp" in package lpSolve, regarding "const.dir" it says: const.dir: Vector of character strings giving the direction of the constraint: each value should be one of "<," "<=," "=," "==," ">," or ">=". (In each pair the two values are identical.) I am having trouble understanding

Hi. I am trying to use the linear optimizer from package lpSolve in R 2.4.1 on Windows XP (Version 5.1). The problem I am trying to solve has 2843 variables (2841 integer, 2 continuous) and 8524 constraints, and I have 2 Gb of memory. After I load the input data into R, I have at most 1.5 Gb of memory available. If I start the lp with significantly less memory available (say 1 Gb), I get

Dear all, I am a beginner with lpSolve package (and not an expert in the others). I can not understand why I am doing wrong, and I would be very grateful if anyone could please help me on this. I am trying to optimize ("min") the sum of columns/variables, constrained to >=1. Each column/variables has its weight - given by values at f.obj. The matrix for numeric constraint

Dear UseRs, I wrote following function in order to solve Data Envelopment Analysis. Reason for posting is that the function is slow when nrow(dat) is large. I wonder if other functions could substitute the for() loop in the code, such as mapply(). Can anybody help to rewrite the dea() function as efficiently as possible? The code is as follows:

Dear all, I'm working on a very complex linear optimization problem using the lp.transport function in lpSolve. My PC has 10 cores, but by default R uses only one of them. Is there a straightforward way to make lp.transport use all cores available? I had a look at "High-performance and parallel computing in R" ( http://cran.r-project.org/web/views/HighPerformanceComputing.html),

Apart from the fact that the statement "such that t1+t2+t3+t4=2970 (as it must)" is not correct, the LP can be implemented as follows: library(lpSolve) LHS <- rbind( c(0,0,0,0, 1, 0, 0,0), c(1,0,0,0,-1, 1, 0,0), c(0,1,0,0, 0,-1, 1,0), c(0,0,1,0, 0, 0,-1,1), cbind(-diag(4),diag(4)), c(0,0,0,0,0,1,0,0), c(0,0,0,0,0,0,1,0), c(0,0,0,0,0,0,0,1) ) RHS <-

Hello! I have a curious problem, which I cannot solve. With my code I solve thousands of small linear programs with the package lpSolve automatically. But R crashes sometimes (~always, but always on different linear programs) in a strange way. For illustration, I tried to prepare a simple example, which shows the nature of the problem. The function aaa (see below) declares some constants (only in

Hi. I've tried to execute &#34;example(lp.assign)&#34; in lpSolve package. However, this solution seemed not to return correct answer, all matrix elements were zeros. Could you give me any &#34;solution&#34; about this? Regards.

Hey, Anyone who knows what the following error report when calling lp() in package lpSolve means? Error: status 3 JT -- View this message in context: http://n4.nabble.com/lpSolve-tp1599380p1599380.html Sent from the R help mailing list archive at Nabble.com.

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 =

Dear R-helpers, I am struggling with an optimization problem at the moment and decided to write the list looking for some help. I will use a very small example to explain what I would like to. Thanks in advance for your help. We would like to distribute resources from 4 warehouses to 3 destinations. The costs associated are as follows: Destination >From 1 2 3 Total

Question for 'experts' in LP using R (using the lpSolve package, say) -- which does not apply to me for the sort of problem I describe below. I've run any number of LP's using lpSolve in R, but all of them to date have objective and constraint functions that both contain the same variables. This lets you set up a LHS and RHS matrix/vector that are symmetrical. But, for a

Hi, I am new to R for solving optimization problems, I have set of communication channels with limited capacity with two types of costs, fixed and variable cost. Each channel has expected gain for a single communication. I want to determine optimal number of communications for each channel maximizing ROI)return on investment) with overall budget as constraint.60000 is the budget allocated.

Hello all, I would like to use the lp function (lpSolve package) on a linux system. Using sample code from the lp function help, I can solve the linear program with no errors or problems. However, when I copy the exact same code to R in linux (after loading the lpSolve package), I get the following error: *** caught segfault *** address 0x3, cause 'memory not mapped' Traceback: 1:

Dear all, I am trying to look for a built in function that performs the cochran Q test. that is, cochranq.test(X) where X is a contingency table (maybe a matrix or data.frame). The output will naturally be the test statisitcs, p-value, etc. A quick search on Google gives me the cochran.test in the 'outlier' package, but I had a look at the description of the test and it doesn't look

How to define bounds for a semi continous variable in lp_solve. Min 5x1 +9x2 +7.15x3 +0.1x4 subject to x1+x2+x3+x4=6.7 x1+x4 <= 6.5 And x3 can be 0 or greater than 3.6 hence x3 is a semi continous variable how to define bounds as well as semicontinous function because using set.semicont and set. bound simantaneously doesn't seem to work.Thanks in advance for the help -- View this

Hello R users, I have two quick questions while using "lpSolve" package for linear programming. (1) the result contains both characters and numbers, e.g., Success: the objective function is 40.5, but I only need the number, can I only store the number? (2) How to set boundaries for variables? e.g., all variable are positive. Thanks a lot! Xiaoxi

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