similar to: QCQP Optimization

I'm interested in the Rsolnp package. For their primary function "solnp", one example is given, and there is a reference to "unit tests". Anyone know where these can be found? Also, Rsolnp is used in a few other packages (e.g., depmixS4), but I cannot seem to find source illustrating its call sequence, and the precise definition of the functions passed. Can anyone help?

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

Hi list, I just received a storage array and created a 3TB LUN with a block/sector size on the disks of 4096. When I boot the our centos-4.4 box attached to this array I get the "unsupported sector size 4096" message: Remounting root filesystem in read-write mode: [ OK ] ERROR: unsupported sector size 4096 on /dev/sda. No RAID disks Setting up Logical Volume Management: [ OK ]

Hi all, Does anyone have any tips for extracting chunks of data from a distance matrix? For instance, if one was interested in only a subset of distance comparisons (i.e., that of rows 4 thru 6, and no others), is there a simple way to pull that data out? >From some playing around with an example (below), I've been able to figure out that a distance matrix in R is stored as a single

Dear, I am using the solnp command (package Rsolnp) for a problem with equality and inequality constraints. I am not getting convergence for my problem but apart that I get 1 warning per iteration saying: ?In cbind(temp, funv) : number of rows of result is not a multiple of vector length (arg 1)?. I checked for equality and inequality functions and they seem fine to me. Where this

Hi I'm having problems getting the "factorized" argument in solve.QP (part of the quadprog library) to work as expected. The helpfile states that when the factorized argument is set to TRUE, then the function requires the inverse of a square-root factor of the Hessian instead of the Hessian itself. That is, when factorized=TRUE, the Dmat argument should be a matrix R^(-1), such

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

(Warning: I'm not an R guy. I'm a Python guy trying to get the R-Python interface working again after some upgrades.) I'm trying to upgrade our numpy/rpy/matplotlib environment (Solaris 10/Intel, Python 2.4). In the process I found I needed to rebuild R (2.1.1) because it was compiled with gcc 3.3.2 and we have since migrated to gcc 3.4.1. I'm using this configure setup:

Hi all, Could anybody please point me to the solver function in R on QCQP? The quadprog package seems to be only able to handle the linear constraints... Thank you! [[alternative HTML version deleted]]

Hello. I'm trying to solve a quadratic programming problem of the form min ||Hx - y||^2 s.t. x >= 0 and x <= t using solve.QP in the quadprog package but I'm having problems with Dmat not being positive definite, which is kinda okay since I expect it to be numerically semi-definite in most cases. As far as I'm aware the problem arises because the Goldfarb and Idnani method first

I was playing around with a simple example using solve.qp ( function is in the quadprog package ) and the code is below. ( I'm not even sure there if there is a reasonable solution because I made the problem up ). But, when I try to use solve.QP to solve it, I get the error that D in the quadratic function is not positive definite. This is because Dmat is zero because I don't have a

Dear All, I have a pair of .cpp and .def file can be compiled using VC++ and works perfectly well in S-PLUS. I wanted to do the same for R; so I followed the guidline given in "Building from a source-code library under Windows" as much as possible and manage to compile them using VC++ and call it from R. But it gives different answer from the one called from S-Plus. I know that I did

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

Hello: Does anyone know if there exists a package that handles methods for [ for dist objects? I would like to access a dist object using matrix notation e.g. dMat = dist(x) dMat[i,j] Thanks in advance to anyone who can point me in the right direction. [[alternative HTML version deleted]]

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

Hello, here is an example from the manual. How to turn this minimization problem into maximization problem, i.e. -(0 5 0) %*% b - 1/2 b^T b? # Assume we want to minimize: -(0 5 0) %*% b + 1/2 b^T b # under the constraints: A^T b >= b0 # with b0 = (-8,2,0)^T # and (-4 2 0) # A = (-3 1 -2) # ( 0 0 1) # we can use solve.QP.compact as follows: # library(quadprog) Dmat <- matrix(0,3,3)

Hi - First posting here. I am using fPortfolio to try and optimize a simple portfolio consisting of 5 daily return series. I want to maximize return subject to setTargetRisk(myspec)=0.08 using only constraints="LongOnly" I can run feasiblePortfolio() using a spec file that specifies the weights, and it works fine. When I run maxreturnPortfolio(mydata,myspec,"LongOnly"),

I got following error while I was using solve.QP() in my problem: > Dmat = matrix(c(0.0001741, 0.0001280, 0.0001280, 0.0002570), nrow=2) > dvec = t(c(0,0)) > Amat = matrix(c(-1,1,0,-1,0, 1,0,1,0,-1), nrow=5) > bvec = c(-20000, 1, 1, -50000, -50000) > solve.QP(Dmat,dvec,Amat,bvec=bvec) Error in solve.QP(Dmat, dvec, Amat, bvec = bvec) : Amat and dvec are incompatible! >

Hello to everybody, I have a quadratic programming problem that I am trying to solve by various methods. One of them is to use the quadprog package in R. When I check positive definiteness of the D matrix, I get that one of the eigenvalues is negative of order 10^(-8). All the others are positive. When I set this particular eigenvalue to 0.0 and I recheck the eigenvalues in R, the last

Hi, I have a least square fitting problem with linear inequality constraints. pcls seems capable of solving it so I tried it, unfortunately, it is stuck with the following error: > M <- list() > M$y = Dmat[,1] > M$X = Cmat > M$Ain = as.matrix(Amat) > M$bin = rep(0, dim(Amat)[1]) > M$p=qr.solve(as.matrix(Cmat), Dmat[,1]) > M$w = rep(1, length(M$y)) > M$C = matrix(0,0,0)