I am trying to minimize a quadratic program with quadratic constraints but I
am having trouble choosing the package to use. I have been reading the
documentation and it seems like all the examples use equations instead of
vector manipulation. All of my parameters are vectors and matrices and they
can be quite large. Here is my problem:
X<-([Cf]+[H])%*%[A]
Y<-([Cf]+[H]-[R])%*%[B]I want to find H that minimizes Y%*%Dmat%*%t(Y) for a
given value of X%*%Dmat%*%t(X)
Cf, R, A, Dmat and B are matrices of constants.
The values for H sohould be between 0 and 1.
Is it possible to use Rsolnp to find the vector H even though the input
functions will all return other vectors?
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On Oct 7, 2014, at 10:59 AM, BuffaloFan32 <jweiner at
tulane.edu<mailto:jweiner at tulane.edu>> wrote:
I am trying to minimize a quadratic program with quadratic constraints but I
am having trouble choosing the package to use. I have been reading the
documentation and it seems like all the examples use equations instead of
vector manipulation. All of my parameters are vectors and matrices and they
can be quite large. Here is my problem:
X<-([Cf]+[H])%*%[A]
Y<-([Cf]+[H]-[R])%*%[B]I want to find H that minimizes Y%*%Dmat%*%t(Y) for a
given value of X%*%Dmat%*%t(X)
Cf, R, A, Dmat and B are matrices of constants.
The values for H sohould be between 0 and 1.
Is it possible to use Rsolnp to find the vector H even though the input
functions will all return other vectors?
--
View this message in context:
http://r.789695.n4.nabble.com/QCQP-Optimization-tp4698012.html
Sent from the R help mailing list archive at
Nabble.com<http://Nabble.com>.
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