similar to: The quadprog package

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

Dear list members, I am trying to get information on how to fit a linear regression with constrained parameters. Specifically, I have 8 predictors , their coeffiecients should all be non-negative and add up to 1. I understand it is a quadratic programming problem but I have no experience in the subject. I searched the archives but the results were inconclusive. Could someone provide suggestions

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 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 <-

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

Hello everyone, I have 3 variables Y, X1 and X2. Each variables lies between 0 and 1. I want to do a constrained regression such that a>0 and (1-a) >0 for the model: Y = a*X1 + (1-a)*X2 I tried the help on the constrained regression in R but I concede that it was not helpful. Any help is greatly appreciated -- Thanks, Jim. [[alternative HTML version deleted]]

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!

Hi All, I'm trying to do the following constrained optimization example. Maximize x1*(1-x1) + x2*(1-x2) + x3*(1-x3) s.t. x1 + x2 + x3 = 1 x1 >= 0 and x1 <= 1 x2 >= 0 and x2 <= 1 x3 >= 0 and x3 <= 1 which are the constraints. I'm expecting the answer x1=x2=x3 = 1/3. I tried the "constrOptim" function in R and I'm running into some issues. I first start off

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

Hi there, I've got the following problem under Windows XP, R 2.5.1: When I'm pasting some objects: Stadtwerksname<-"Mannheim" Laufwerk<-"C:\\" paste(Laufwerk,Stadtwerksname,".csv") I get the result: [1] "C:\\ Mannheim .csv" The problem's are the superfluous gaps/blanks between the three parts. Is there a way to get rid off this

Dear all, I'm sitting here just in front of my new PC@work and wonder about the following question: * How can I adress multiple CPUs (cores) out of R to speed up the simulations I run? * What are the prerequisites to do so? Maybe anyone could give me a hint where to start reading? Regards, Thomas P.S.: I searched the R-archive to find an answer but did find none.

Hi all I have the following code in my controller: if @comment.valid? captcha_url = "http://captchator.com/captcha/check_answer/#{captcha_code}/#{@comment.captcha}" result = open(captcha_url) unless result.read == "1" @comment.errors.add(:captcha, "Captcha wurde nicht korrekt eingegeben") raise "#{@comment.valid?}"

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

Hi there, I have a major problem (major for me that is) with solve.QP and I'm new at this. You see, to solve my quadratic program I need to have the lagrange multipliers after each iteration. Solve.QP gives me the solution, the unconstrained solution aswell as the optimal value. Does anybody have an idea for how I could extract the multipliers? Thanx, Serge "Beatus qui prodest quibus

Hi everyone, I did my homework and read the posting guideline :-) I want to eMail the results of a computing automatically. So I get the results (the parameters of a garch process) and I want to eMail them to another person. How can I do that? Thx ______________________________ Thomas Schwander MVV Energie Konzern-Risikocontrolling Telefon 0621 - 290-3115 Telefax 0621 - 290-3664 E-Mail:

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

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

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, 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]]