[R-SIG-Finance] efficient linear programming problem!

[Alexios Ghalanos]

Quick suggestion: If there is sparsity in your matrix you may benefit from using slam (Rsymphony and Rglpk both accept slam matrices).

-Alexios

On 10 Jul 2013, at 12:50, Daniel Duan <elephann at gmail.com> wrote:

> Dear all,
>  I am dealing with a Linear Programming problem which involve intensive
> caculation and my code seems quite inefficient: it take 10 to 30 minutes to
> finish a single optimization. I wonder can anyone give me some advice on
> how
> to improve it. Any suggestions as to packages or code that will work
> efficiently for my problem would be much appreciated.
> Thanks!
> Here are my code:
> data: rmat is a time series object with s row and n column(or s*n
> matrix,s=130,n=30)
> memory.limit(12000)
> require(Rsymphony)
> 
> P.risk_parameter<-c(106,44,4,3)
> # parameter
> 
> PRAF10<-
> function(p,risk_parameter=P.risk_parameter[2]){risk_parameter*(1-p)^(risk_parameter-1)}
> # function
> 
> Amat.function<-function(rmat,RFR=0){
> n = ncol(rmat)
> s = nrow(rmat)
> # construct constraint matrix
> A<-matrix(0,2*s*s+3,s*s+s+n+1)
> A[1,1:n]<-                              colMeans(rmat)
> A[1,s*s+s+n+1]<-                        -RFR
> A[2,1:n]<-                              1
> A[2,s*s+s+n+1]<-                        -1
> for(i in 1:s)(A[(3+(i-1)*s):(i*s+2),1:n]<-rmat )
> for(i in 1:s)(A[(3+(i-1)*s):(i*s+2),n+i]<- -1)
> A[3:(s*s+2),(n+s+1):(s*s+s+n)]<-        diag(s*s)
> A[(3+s*s):(2*s*s+2),(n+s+1):(s*s+s+n)]<-diag(s*s)
> A[3:(2*s*s+2),s*s+s+n+1]<-              -RFR
> A[2*s*s+3,s*s+s+n+1]<-                   1
> return(A)
> }
> 
>    [[alternative HTML version deleted]]
> 
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