similar to: BVLS

A new package 'bvls' is available on CRAN. The package provides an R interface to the Stark-Parker algorithm for bounded-variable least squares (BVLS) that solves A x = b with the constraint l <= x <= u under least squares criteria, where l,x,u \in R^n, b \in R^m and A is an m \times n matrix. The Stark-Parker BVLS algorithm was published in Stark PB, Parker RL (1995).

A new package 'bvls' is available on CRAN. The package provides an R interface to the Stark-Parker algorithm for bounded-variable least squares (BVLS) that solves A x = b with the constraint l <= x <= u under least squares criteria, where l,x,u \in R^n, b \in R^m and A is an m \times n matrix. The Stark-Parker BVLS algorithm was published in Stark PB, Parker RL (1995).

This is what to start with: Data Frame A B C D c1 4 y 5 c3 6 d 7 c1 5 t 6 Now sort on A then C This is what to end with: Data Frame A B C D c1 5 t 6 c1 4 y 5 c3 6 d 7 I assume it is something like this: attach(DF) sort(DF,partial=c(A,C)) Thanks in advance. Meredith [[alternative HTML version deleted]]

Hi, Thanks for reading. I am running a process in R for microarray data analysis. RedHat Enterprise Linux 4, dual AMD CPU, 6G memory. However, the R process use only a total of <200M memory. And the CPU usage is total to ~110% for two. The program takes at least 2 weeks to run at the current speed. Is there some way we can increase the usage of CPUs and memories and speed up? Any

Thanks Mike. My data has longitude and latitude coords and I used distAB {clim.pact} then mst {ape} to calculate my minimum spanning tree. The nodes are telecoms sites from all over Australia. My goal is to determine the minimum cost of linking them via cabling, and I'm starting by calculating the distance "as the crow flies", but will probably eventually need to calculate the

Hello How do I use function 'MERGE" to combine the FILE A and FILE B below to make FILE C? Thank you FILE A 140 151 167 30.1 11.4 40 FILE B 140 167 5.7 30.3 FILE C 140 151 167 30.1 11.4 40 5.7 NA 30.3

Hi I am trying to print out means, STDs and histograms under two sets of factors. I can manage it for one set - see below but not for two sets. That is, I want ot print out the mean STD and Histogram for each ITEM code within each DELIVERABLE code. In addition I can only get to view the histogram for the last item. How do you get R to stop overriding the histogram for eg level 1 for factor 1

Hi, I have been brought back to the "R-Side" from MatLab. I have used R in graduate econometrics but only for statistics and regression (linear and nonlinear). But now I need to run general nonlinear optimization. I know about the add-in quadprog but my problem is not QP. My problem is a general nonlinear (obj funct) with linear constraints.I know about the "ms" and

At the risk of ridicule for my deficient linear algebra skills, I ask for help using the solve.QP function to do portfolio optimization. I am trying to following a textbook example and need help converting the problem into the format required by solve.QP. Below is my sample code if anyone is willing to go through it. This problem will not solve because it is not set up properly. I hope I

Hello I have to show that R has more functionality than SAS for MonteCarlo type exercises involving matrix algebra. As I has little experience with SAS is anyone able to help. I have a model written in R but the IT world in this company is not familiar with R and wants to use something like SAS. thanks

Dear list, I am trying to follow an example that estimates a 2x2 markov transition matrix across several periods from aggregate data using restricted least squares. I seem to be making headway using solve.QP(quadprog) as the unrestricted solution matches the example I am following, and I can specify simple equality and inequality constraints. However, I cannot correctly specify a constraint

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 everybody, I'm using Windows XP Prof, R 2.5.1 and a Pentium 4 Processor. Now, I want to solve a quadratic optimization program (Portfolio Selection) with the quadprog package I want to minimize (\omega'%*%\Sigma%*%\omega) Subject to (1) \iota' %*% \omega = 1 (full investment) (2) R'%*%\omega = \mu (predefined expectation value) (3) \omega \ge 0 (no short sales). Where

Hi, I am using solve.QP (from quadprog) to solve a standard quadratic programming problem: min_w -0.5*w'Qw st ... I would like solve.QP to do two things: 1) to start the optimization from a user-supplied initial condition; i.e., from a vector w_0 that satisfies the constraints, and 2) to return the values of the lagrange multiplieres associated with the constraints. I did not find an obvious

hello all, i'm trying to use QPmat, from the popbio package. it appears to be based on solve.QP and is intended for making a population projection matrix. QPmat asks for: nout, A time series of population vectors and C, C constraint matrix, (with two more vectors, b and nonzero). i believe the relevant code from QPmat is: function (nout, C, b, nonzero) { if (!"quadprog" %in%

Hi, I'm using the package quadprog to solve the following quadratic programming problem. I want to minimize the function (b_1-b_2)^2+(b_3-b_4)^2 by the following constraints b_i, i=1,...,4: b_1+b_3=1 b_2+b_4=1 0.1<=b_1<=0.2 0.2<=b_2<=0.4 0.8<=b_3<=0.9 0.6<=b_4<=0.8 In my opinion the solution should be b_1=b_2=0.2 und b_3=b_4=0.8. Unfortunately R doesn't find

I'm having difficulty figuring out how to implement the following set of constraints in Quadprog: 1). x1+x2+x3+x4=a1 2). x1+x2+x5+x6=a2 3). x1+x3+x5+x7=a3 4). x1+x2=b1 5). x1+x3=b2 6). x1+x5=b3 for the problem: MIN (x1-c1)2+(x2-c2)2+...+(x8-c8)2. As far a I understand, "solve.QP(Dmat, dvec, Amat, bvec, meq=0, factorized=FALSE)" reads contraints using an element-by-element

I am having some problems using Quadprog in R. I want to minimize the objective function : 200*P1-1/2*10*P1^2+100*P2-1/2*5*P2^2+160*P3-1/2*8*P3^2+50*P4-1/2*10*P4^2+50*P 5-1/2*20*P5^2+50*P6-1/2*10*P6^2, Subject to a set of constrains including not only the variables P1, P2, P3, P4, P5, P6, but also the variables X1, X2,X3,X4,X5,X6,X7,X8,X9. As the set of variables X's are not

gday R gurus, I have a multivariate regression for which I want to constrain the coefficients to be > 0. Is this possible? I've check the doco and searched CRAN but can't find anything. thanks, John Strumila -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html Send

exportData(MU.Cost,paste("C:/RAUDSL/S",as.character(MU.Cost$Run.Id[1])," .",as.character(MU.Cost$MU.Id[1]),".MU.PRICE.OUTPUT.txt",sep=""),append = FALSE,type="ASCII",quote=FALSE) [[alternative HTML version deleted]]