similar to: Linear Optimization

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 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, 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

(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:

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

Dear all, I need to maximize the v: v= D' W D D is a column vector ( n , 1) W is a given matrix (n, n) subject to: sum D= 1 (BTW, n is less than 300) I´ve tried to use maxBFGS, as follows: ##################################### objectiveFunction<-function(x) { return(t(D)%*%W%*%D) } Amat<-diag(nrow(D)) Amat<-rbind((rep(-1, nrow(D))), Amat) bvec<-matrix( c(0), nrow(D)+1,

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 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 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 <-

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

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

Hello, I am using constrOptim to maximize a likelihood function (the values of my parameter vector must be between zero and one and must sum up to <=1). I am getting the error 'initial value in 'vmmin' is not finite'. I've tracked it down to a problem in the function 'R' defined within the constrOptim function. It is performing a check on my values, and its not

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

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)

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

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"),

Hi All, I've hit a problem using nls. I think it may be a restriction in the applicability of nls and I may have found a fix, but I've been wrong before. This example is simplified to the essentials. My real application is much more complicated. Take a function of matrix 'x' with additional arguments: matrix 'aMat' whose values are _not_ to be determined by nls vector

Hi, I wrote a package for linear programming and want to submit it to CRAN. Since the package 'quadprog' has a function with the name 'solve.QP' to perform Quadratic Programming, I named my (main) function 'solve.LP'. However 'R CMD check' gives one warning: * checking generic/method consistency ... WARNING solve: function(a, b, ...) solve.LP: