efficient linear programming problem!

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Try rglpk.  In my experience it is faster than Rsymphony
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)
  }

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Brian G. Peterson
http://braverock.com/brian/
Ph: 773-459-4973
IM: bgpbraverock
Quick suggestion: If there is sparsity in your matrix you may benefit from using slam (Rsymphony and Rglpk both accept slam matrices).

-Alexios

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)
}

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