General goal: Write R code to find the inverse matrix of an nxn positive definite symmetric matrix. Use solve() to verify your code works.
Started with a 3x3 matrix example to build the code, but something dosen't seem to be working. I just don't know where I am going wrong. ##Example matrix I found online A<-c(4,1,-1,1,2,1,-1,1,2) m<-matrix(A,nrow=3,ncol=3) ##Caculate the eigen vectors and eigenvalues E<-eigen(m, sym=TRUE) Q<-E$vectors V<-E$values n<-nrow(m) ##normalize the eigenvectors for(i in 1:n){ Q[,i]<-Q[,i]/sqrt(sum(Q[,i]^2)) } ##verify dot product of vectors are orthogonal sum(Q[,1]*Q[,2]) sum(Q[,1]*Q[,3]) sum(Q[,2]*Q[,3]) ##Begin creating QDQ^T matrix. Where Q are orthonormal eigenvectors, and D is a diagonal matrix with 1/eigenvalues on the diagonal. and Q^T is the transpose of Q. R<-t(Q) D<-mat.or.vec(n,n) for(i in 1:n) { D[i,i]<-1/V[i] } P<-Q*D*R ## P should be the inverse of the matrix m. Check using solve(m) ## solve(m) does not equal P? Any ideas of what I am missing/not understanding? -- View this message in context: http://r.789695.n4.nabble.com/Inverse-matrix-using-eigendecomposition-tp4188673p4188673.html Sent from the R help mailing list archive at Nabble.com. ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.