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Dr Hosking, interested in coming to this? Michael Goldfarb, interested in coming to this? -- Sean Silva On Sat, Nov 17, 2012 at 12:53 AM, Joe Abbey <jabbey at arxan.com> wrote: > First LLVM Social in West Lafayette, IN. > > When: > Thursday, Dec 6th @ 8:30pm > > Where: > GreyHouse Coffee > 100 Northwestern Avenue > West L...

...he 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(...

First LLVM Social in West Lafayette, IN. When: Thursday, Dec 6th @ 8:30pm Where: GreyHouse Coffee 100 Northwestern Avenue West Lafayette, IN 47906 https://plus.google.com/102658466942690664690/about Please RSVP, so I can let GreyHouse know if we need a meeting room. Cheers, Joe ______________________________ Joe Abbey Director of Software Development Arxan Technologies jabbey at

...ot;u1","s1","u2","s2"), function(x,p,u1,s1,u2,s2) NULL) mix.gr<-function(p,x){ p<-p[1] u1<-p[2] s1<-p[3] u2<-p[4] s2<-p[5] colSums(attr(lmix2a(x,p,u1,s1,u2,s2),"gradient"))} # finally, the optimization using the Broyden-Fletcher-Goldfarb- Shanno method. resultsBFGS_D=optim (p0,mix.obj,mix.gr,x=waiting,method="BFGS",control=list(parscale=c (0.1,rep(1,4)))) resultsBFGS_D$par CODE END The output is given as: p u1 s1 u2 s2 0.361204 50.000000 5.000000 80.0000...

Hi, I am using solve.QP (from quadprog) to solve a standard quadratic programming problem: min_w -0.5*w'Qw st ... I would like solve.QP to do two things: 1) to start the optimization from a user-supplied initial condition; i.e., from a vector w_0 that satisfies the constraints, and 2) to return the values of the lagrange multiplieres associated with the constraints. I did not find an obvious

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