similar to: not positive definite D matrix in quadprog

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

Oops... just realized there were a couple mistakes in the question I posted regarding installation of quadprog: 1) The gcc command line is shown wrapped to the next line. Please ignore this. 2) In addition to adding a -Lc:/apps/rw1050/bin option to the command line, I also added -lRblas. Thanks, -jh- ================================= John Heumann, Agilent Technologies,

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

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 <-

I upgraded to R-2.2.1 on two PPC G5 computers today. Further I want to work with the tseries package for the first time. As root with R CMD INSTALL tseries_0.10-0.tar.gz I get the following gcc-3.3 -bundle -flat_namespace -undefined suppress -L/usr/local/lib - o tseries.so arma.o bdstest.o boot.o dsumsl.o garch.o ppsum.o tsutils.o -framework vecLib -L/usr/local/lib/gcc/powerpc-apple-

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

When I try to build/install the quadprog package under R1.50 and NT4 it seems to have trouble finding blas routines. I'm currently using the generic blas routines, rather than ATLAS or some other tuned implementation: > Rcmd INSTALL d:/temp/quadrprog ... gcc --shared -s -o quadprog.dll quadprog.def quadprog.a quadprog_res.o -Lc:/a pps/rw1050/src/gnuwin32 -lg2c -lR

Full_Name: Ansgar Steland Version: 0.90.0 OS: Linux 6.1 FreeBSD 3.2 Submission from: (NULL) (62.104.196.10) Dear R Team, Yesterday I downloaded R 0.90.0 and the current versions of some packages (tseries, quadprog,...). I had no problems to build the program using FreeBSD 3.2 and SuSe Linux 6.1. I also re-build all packages required by tseries. I checked out portfolio.optim (package:

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 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

I'm trying to test if a correlation matrix is positive semidefinite. My understanding is that a matrix is positive semidefinite if it is Hermitian and all its eigenvalues are positive. The values in my correlation matrix are real and the layout means that it is symmetric. This seems to satisfy the Hermitian criterion so I figure that my real challenge is to check if the eigenvalues are all

hHllo, I'm looking for an algroithm to transform an existing toeplitz matrix (autocorrelation matrix) to the nearest positive semidefinite toeplitz matrix. I merely found an algorithm to transform an correlation matrix via the function nearcor() based on the algorithm of Higham. But as I examined, it destroys the toeplitz structure of my underlying matrix. Does any function already exist

Hey guys, I'm trying to run a Canonical Correlation Analysis (CCA) between two data sets. But for my case I am dealing with data sets(which are stored as two matrices X and Y) in which the number of experimental units is greater than the number of variables, so I want to use a sample from my "mothdata" data set which I store in a matrix called "Yr". I am using the cc()

Is there a function in r that let's you efficiently invert a positive definite symmetric Block Toeplitz matrix? My matrices are the covariance matrices of observations of a multivariate time series and can be 1000*1000 or larger. I know the package 'ltsa' which seems to use the Trench algorithm to compute the inverse of a Toeplitz matrix. I am looking for a so to say

Hey, all Given a square matrix, how can I check if this matrix is positive definite or not? Thanks. Fred

Am I correct to understand from the previous discussions on this topic (a few years back) that if I have a matrix with missing values my PCA options seem dismal if: (1) I don’t want to impute the missing values. (2) I don’t want to completely remove cases with missing values. (3) I do cov() with use=”pairwise.complete.obs”, as this produces negative eigenvalues (which it has in

I want to compute B=A^{1/2} such that B*B=A. For example a=matrix(c(1,.2,.2,.2,1,.2,.2,.2,1),ncol=3) so > a [,1] [,2] [,3] [1,] 1.0 0.2 0.2 [2,] 0.2 1.0 0.2 [3,] 0.2 0.2 1.0 > a%*%a [,1] [,2] [,3] [1,] 1.08 0.44 0.44 [2,] 0.44 1.08 0.44 [3,] 0.44 0.44 1.08 > b=a%*%a i have tried to use singular value decomposion > c=svd(b) > c$u%*%diag(sqrt(c$d))

I am creating covariance matrices from sets of points, and I am having frequent problems where I create matrices that are non-positive definite. I've started using the corpcor package, which was specifically designed to address these types of problems. It has solved many of my problems, but I still have one left. One of the matrices I need to calculate is a cross-covariance matrix. In other

Hello, i calculate with the formula conf=Sum(2*Xn^n) definite number for binary configurations . How it is possible to count the number of different conf ? example: 384 384 512 512 512 the result should be 2 . Thanks for advance & regards, Christian -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read