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 solves the unconstrained problem requiring a positive definite matrix. Are there any (fast) packages that allows me to do QP with (large) semidefinite matrices?
Example: t <- 100 y <- signalConvNoisy[1,] D <- crossprod(H,H) d <- crossprod(H,y) A <- cbind(rep(-1, nrow(H)), diag(ncol(H))) b0 <- c(t, rep(0, ncol(H))) sol <- solve.QP(Dmat=D, dvec = d, Amat = A, bvec = b0)$solution Error in solve.QP(Dmat = D, dvec = d, Amat = A, bvec = b0) : matrix D in quadratic function is not positive definite! Thanks in advance, Andreas Jensen ______________________________________________ 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.
