similar to: Is it solve.QP or is it me?

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

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

I forgot to attach the problem data, 'quadprog.Rdata' file, in my prior email. I want to report a following error with quadprog. The solve.QP function finds a solution to the problem below that violates the last equality constraint. I tried to solve the same problem using ipop from kernlab package and get the solution in which all equality constraints are enforced. I also tried an old

For my problem I have #Constraints b11<-0 b21<-0 b12<-0 b22<-0 n1>=0 n2>=0 n1<=1 n1<=1 n1+n2=1 In order to use spg I set Amat<-matrix(rbind(c(rep(0,10)),c(rep(0,10)),c(rep(0,2),1,rep(0,7)), c(rep(0,3),1,rep(0,6)),c(rep(0,10)),c(rep(0,10)), c(rep(0,6),1,rep(0,3)),c(rep(0,7),1,rep(0,2)), c(rep(0,8),1,rep(0,1)),c(rep(0,9),1,rep(0,0)),

Hi list: This is my first time to post my question on the list. Thanks for your help. I am solving a quadratic programming using R. Here is my question: w = arg min 0.5*w'Mw - w'N s. t. sum(w) = 1; w>0 note: w is weight vector, each w_i must >=0, and the sum of w =1. Here is my R code: A <-matrix(c(2.26,1.26,1.12,1.12,2.27,1.13,1.12,1.13,2.2),3,3); B <-

Im trying to do regressions with constraints that the weights are all >=0 and sum(weights) = 1. I've read the archive and have set the problem up with solve.QP and just the non-negativity constraints along the lines of: y as the data vector X as the design matrix D <- t(X) %*% X d <- t(t(y) %*% X) A <- diag(ncol(X)) b <- rep(0,ncol(X)) fit <-

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, You can use the projectLinear argument in BB::spg to optimize with linear equality/inequality constraints. Here is how you implement the constraint that all parameters sum to 1. require(BB) spg(par=p0, fn=myFn, project="projectLinear", projectArgs=list(A=matrix(1, 1, length(p0)), b=1, meq=1)) Hope this is helpful, Ravi [[alternative HTML version deleted]]

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

Hi George, I already outlined the drbd-stuff on xen-users (see attachement). Yes - elrepo is right on mark. Excerpt: "The script (/etc/xen/scripts/block-drbd) I am using is from drbd84-utils-8.9.2-1.el6.elrepo.x86_64 - and already seems to contain the "drbd or phy" case; both in the add and the remove branch. CentOS6 XEN4 is based on the Kernel 3.10.68-11.el6.centos.alt.x86_64

Hello everyone, we try to use a server running linux in an NT-domain and get the error message given in the subject-line. More details from the logfile and smb.conf are given below. Can anyone give us a hint what the problem is? (Of course...) The problem is very urgent for us. Thanks a lot in advance. Helmut Rickel ================= smb.conf ======================== [global]

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 - 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"),

Thank you Brian Ripley, Gardar Johannesson, and Marcel Wolbers for your prompt and friendly help! I will share any further learnings as I move through these suggestions. -Bob Abugov Brian Ripley wrote: I just use optim() on the sum of squares with non-negativity constraints. That did not exist in 1999. Gardar Johannesson wrote: You can always just use the quadratic programing library in R

I have a set of data for US t-bill returns and US stock returns frm 1980-2012. I am trying to bootstrap the data and obtain the minimum variance portfolio and repeat this portfolio 1000 times. However I am unable to get the correct code function for the minimum variance portfolio. When I tried to enter Opt(OriData+1, 1, 5, 0), I get "error:subscript out of bounds" Please help!

Hello R, I am looking for non-parametric simulation in IRT. Is there any IRT package that does non-parametric simulation? helen L [[alternative HTML version deleted]]

Dear R Users , As a beginner in QP, I'm trying to solve a Support Vector Machine problem by a QP. In particulare I am using the quadprog package. My questions are here: 1- In the document for the package (The quadprog Package), the inequality constraint is mentioned with >= , however in a standard QP, this usaully is written with <= . This constraint should be multiplied by a

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

Dear R users, I'm trying to run the Support Vector Regression by a general quadratic programming function like ipop ( ) in kernlab or solve.QP ( ) in quadprog packages. Since they are general, their application in Support Vector Regression can lead to misunderstanding, particularly when constructing matrices. Even their examples are general and applied in Support Vector

I was thinking about my solve.qp problem on my way home tonight and I think I can fix it by making Dmat the identity matrix. I'll check this weekend to make sure but my thinking is that doing this will make all the "variances" the same so that they shouldn't come into play during the evaluation of the objective function. Thanks though for any confirmation of this or other