efficient linear programming problem!

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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