search for: semidefiniteness

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

Recently, a package for convex optimization was announced for Python, based upon the LP solver GLPK, the SDP solver in DSDP5, and the LP and QP solvers in MOSEK. I'm aware GLPK is available for R, but wondered if anyone had good packages for convex optimization along these lines for R. TIA.

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

Hi, I would like to generate a correlation matrix with a particular structure. For example, a 3n x 3n matrix : A_(nxn) aI_(nxn) bI_(nxn) aI_(nxn) A_(nxn) cI_(nxn) aI_(nxn) cI_(nxn) A_(nxn) where - A_(nxn) is a *specified* symmetric, positive definite nxn matrix. - I_(nxn) is an identity matrix of order n - a, b, c are (any) real numbers Many attempts have been unsuccessful because a

I got the following error: a = read.csv("mat.csv") b = as.matrix(a) tb = t(b) bb = tb %*% b dim(bb) ibb = solve(bb) bb %*% ibb > ibb = solve(bb) Error in solve.default(bb) : system is computationally singular: reciprocal condition number = 1.77573e-19 > Are there any ways to find more information about why it is singular? Thanks.

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 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

Dear All I am trying to do a partial canonical analysis of principal coordinates using Bray-Curtis distances. The capscale addin to R appears to be the only way of doing it, however, when I try and calculate a Bray-Curtis distance matrix either using Capscale or Vegedist (capscale I understand uses Vegedist anyway to calculate its distance matrix), R uses up all available memory on the computer,

Dear R-devel: I could use some advice about matrix calculations and steps that might make for faster computation of generalized inverses. It appears in some projects there is a bottleneck at the use of svd in calculation of generalized inverses. Here's some Rprof output I need to understand. > summaryRprof("Amelia.out") $by.self self.time self.pct

..." package has been split into three packages: miscTools, micEconAids, and micEcon. a) miscTools (version 0.6-0) includes miscellaneous small tools and utilities that are not related to (micro)economics, e.g. colMedians(), rowMedians(), insertCol(), insertRow(), vecli(), symMatrix(), triang(), semidefiniteness(), compPlot(), and rSquared(). The miscTools package should depend on (almost) no other packages so that it can be used in other packages without increasing the total (recursive!) dependencies of these packages too much. b) micEconAids (version 0.6-0) includes all functions and methods for demand...

Dear all, there are R packages able to simulate or estimate bilinear model for time series? I know it is an open problem, but do exist something for very simplified bilinear models? Alternatively, what kinfd of non linear time series models are performed in R? If R is not able, could someone suggest me for some commercial softwares to deal with bilinear models? i'm afraid of a negative

Dear All, While inverting a matrix the following error appears on my console: Error in solve.default(my_matrix) : Lapack routine dgesv: system is exactly singular With this respect, I have been replacing the solve() function with ginv(): the Moore-Penrose generalized inverse of a matrix. These are the questions I would like to ask you: 1. Would you also replace solve() with ginv() in

..." package has been split into three packages: miscTools, micEconAids, and micEcon. a) miscTools (version 0.6-0) includes miscellaneous small tools and utilities that are not related to (micro)economics, e.g. colMedians(), rowMedians(), insertCol(), insertRow(), vecli(), symMatrix(), triang(), semidefiniteness(), compPlot(), and rSquared(). The miscTools package should depend on (almost) no other packages so that it can be used in other packages without increasing the total (recursive!) dependencies of these packages too much. b) micEconAids (version 0.6-0) includes all functions and methods for demand...

Hi all, I have two matrices: G<-matrix(c(2.0, 0.5, 0.5, 0.5, 2.0, 0.5, 0.5, 0.5,2.0),3,3) P<-matrix(c(1.0, 0.5, 0.5, 0.5, 1.0, 0.5, 0.5, 0.5,1.0),3,3) and I want to run this equation to get a new matrix F: F = [P+2G]^-1/2 P [P+2G]^-1/2 Could someone please tell me how to code this in R? Many thanks in advance for your time. Best wishes, Matt [[alternative HTML version deleted]]

...nt > >X1 and Y1 correlated >X2 and Y2 correlated >X1 and X2 correlated >Y1 and Y2 correlated > >but > >X1 and Y2 uncorrelated >Y1 and X2 uncorrelated > > >One approach is to work out the conditional variance of (X2,Y2) given >(X1,Y1) and check for positive semidefiniteness. You do the math... > >(Some preliminary experiments suggest that the criterion could be >abs(a)+abs(r) <= 1, but don't take my word for it) > >> R-version used : >> --------------- >> Windows version >> R-1.8.1 >> Running on Windows XP > >You...

HI, I am really messing up to make a symmetrical matrix using upper.tri() & lower.tri() function. Here is my code:   > set.seed(1) > mat = matrix(rnorm(25), 5, 5) > mat            [,1]       [,2]       [,3]        [,4]        [,5] [1,] -0.6264538 -0.8204684  1.5117812 -0.04493361  0.91897737 [2,]  0.1836433  0.4874291  0.3898432 -0.01619026  0.78213630 [3,] -0.8356286  0.7383247

Hi everyone: I try to use r to do the Cholesky Decomposition,which is A=LDL',so far I only found how to decomposite A in to LL' by using chol(A),the function Cholesky(A) doesnt work,any one know other command to decomposte A in to LDL' My r code is: library(Matrix) A=matrix(c(1,1,1,1,5,5,1,5,14),nrow=3) > chol(A) [,1] [,2] [,3] [1,] 1 1 1 [2,] 0 2 2

Hi everyone. Based on a dependent variable (y), I'm trying to generate some independent variables with a specified correlation. For this there's no problems. However, I would like that have all my "regressors" to be orthogonal (i.e. no correlation among them. For example, y = x1 + x2 + x3 where the correlation between y x1 = 0.7, x2 = 0.4 and x3 = 0.8. However, x1, x2 and x3

Dear list members, the following hlm was constructed: hlm <- groupedData(laut ~ design | grpzugeh, data = imp.not.I) the grouped data object is located at and can be downloaded: www.anicca-vijja.de/lg/hlm_example.Rdata The following works: library(nlme) summary( fitlme <- lme(hlm) ) with output: ... AIC BIC logLik 425.3768 465.6087 -197.6884 Random effects:

Dear all, Has someone implemented in R (or any other language) Knol DL, ten Berge JMF. Least-squares approximation of an improper correlation matrix by a proper one. Psychometrika, 1989, 54, 53-61. or any other similar algorithm? Best regards Jens Oehlschl?gel Background: I want to factanal() matrices of polychoric correlations which have negative eigenvalue. I coded Highham 2002