# [Rd] An example of very slow computation

peter dalgaard pdalgd at gmail.com
Fri Aug 19 00:37:38 CEST 2011

```On Aug 17, 2011, at 23:24 , Ravi Varadhan wrote:

> A principled way to solve this system of ODEs is to use the idea of "fundamental matrix", which is the same idea as that of diagonalization of a matrix (see Boyce and DiPrima or any ODE text).
>
> Here is the code for that:
>
>
> nlogL2 <- function(theta){
>  k <- exp(theta[1:3])
>  sigma <- exp(theta)
>  A <- rbind(
>  c(-k, k),
>  c( k, -(k+k))
>  )
> 	eA <- eigen(A)
> 	T <- eA\$vectors
> 	r <- eA\$values
> 	x0 <- c(0,100)
> 	Tx0 <- T %*% x0
>
> 	sol <- function(t) 100 - sum(T %*% diag(exp(r*t)) %*% Tx0)
> 	pred <- sapply(dat[,1], sol)
>  	-sum(dnorm(dat[,2], mean=pred, sd=sigma, log=TRUE))
> }
> This is much faster than using expm(A*t), but much slower than "by hand" calculations since we have to repeatedly do the diagonalization.  An obvious advantage of this fuunction is that it applies to *any* linear system of ODEs for which the eigenvalues are real (and negative).

I believe this is method 14 of the "19 dubious ways..." (Google for it) and doesn't work for certain non-symmetric A matrices.

>
> Ravi.
>
> -------------------------------------------------------
> Assistant Professor,
> Division of Geriatric Medicine and Gerontology School of Medicine Johns Hopkins University
>
> Ph. (410) 502-2619
>
>
> -----Original Message-----
> From: r-devel-bounces at r-project.org [mailto:r-devel-bounces at r-project.org] On Behalf Of Ravi Varadhan
> Sent: Wednesday, August 17, 2011 2:33 PM
> To: 'cberry at tajo.ucsd.edu'; r-devel at stat.math.ethz.ch; 'nashjc at uottawa.ca'
> Subject: Re: [Rd] An example of very slow computation
>
> Yes, the culprit is the evaluation of expm(A*t).  This is a lazy way of solving the system of ODEs, where you save analytic effort, but you pay for it dearly in terms of computational effort!
>
> Even in this lazy approach, I am sure there ought to be faster ways to evaluating exponent of a matrix than that in "Matrix" package.
>
> Ravi.
>
> -------------------------------------------------------
> Assistant Professor,
> Division of Geriatric Medicine and Gerontology School of Medicine Johns Hopkins University
>
> Ph. (410) 502-2619
>
> -----Original Message-----
> From: r-devel-bounces at r-project.org [mailto:r-devel-bounces at r-project.org] On Behalf Of cberry at tajo.ucsd.edu
> Sent: Wednesday, August 17, 2011 1:08 PM
> To: r-devel at stat.math.ethz.ch
> Subject: Re: [Rd] An example of very slow computation
>
> John C Nash <nashjc at uottawa.ca> writes:
>
>> This message is about a curious difference in timing between two ways of computing the
>> same function. One uses expm, so is expected to be a bit slower, but "a bit" turned out to
>> be a factor of >1000. The code is below. We would be grateful if anyone can point out any
>> egregious bad practice in our code, or enlighten us on why one approach is so much slower
>> than the other.
>
> Looks like A*t in expm(A*t) is a "matrix".
>
> 'getMethod("expm","matrix")' coerces it arg to a "Matrix", then calls
> expm(), whose method coerces its arg to a "dMatrix" and calls expm(),
> whose method coerces its arg to a 'dgeMatrix' and calls expm(), whose
> method finally calls '.Call(dgeMatrix_exp, x)'
>
> Whew!
>
> The time difference between 'expm( diag(10)+1 )' and 'expm( as( diag(10)+1,
> "dgeMatrix" ))' is a factor of 10 on my box.
>
> Dunno 'bout the other factor of 100.
>
> Chuck
>
>
>
>
>> The problem arose in an activity to develop guidelines for nonlinear
>> modeling in ecology (at NCEAS, Santa Barbara, with worldwide participants), and we will be
>> trying to include suggestions of how to prepare problems like this for efficient and
>> effective solution. The code for nlogL was the "original" from the worker who supplied the
>> problem.
>>
>> Best,
>>
>> John Nash
>>
>> --------------------------------------------------------------------------------------
>>
>> cat("mineral-timing.R == benchmark MIN functions in R\n")
>> #  J C Nash July 31, 2011
>>
>> require("microbenchmark")
>> require("numDeriv")
>> library(Matrix)
>> library(optimx)
>> # t<-dat[,1]
>> t <- c(0.77,  1.69,  2.69,  3.67,  4.69,  5.71,  7.94,  9.67, 11.77, 17.77,
>> 23.77, 32.77, 40.73, 47.75, 54.90, 62.81, 72.88, 98.77, 125.92, 160.19,
>> 191.15, 223.78, 287.70, 340.01, 340.95, 342.01)
>>
>> # y<-dat[,2] # ?? tidy up
>> y<- c(1.396, 3.784, 5.948, 7.717, 9.077, 10.100, 11.263, 11.856, 12.251, 12.699,
>> 12.869, 13.048, 13.222, 13.347, 13.507, 13.628, 13.804, 14.087, 14.185, 14.351,
>> 14.458, 14.756, 15.262, 15.703, 15.703, 15.703)
>>
>>
>> ones<-rep(1,length(t))
>> theta<-c(-2,-2,-2,-2)
>>
>>
>> nlogL<-function(theta){
>>  k<-exp(theta[1:3])
>>  sigma<-exp(theta)
>>  A<-rbind(
>>  c(-k, k),
>>  c( k, -(k+k))
>>  )
>>  x0<-c(0,100)
>>  sol<-function(t)100-sum(expm(A*t)%*%x0)
>>  pred<-sapply(dat[,1],sol)
>>  -sum(dnorm(dat[,2],mean=pred,sd=sigma, log=TRUE))
>> }
>>
>> getpred<-function(theta, t){
>>  k<-exp(theta[1:3])
>>  sigma<-exp(theta)
>>  A<-rbind(
>>  c(-k, k),
>>  c( k, -(k+k))
>>  )
>>  x0<-c(0,100)
>>  sol<-function(tt)100-sum(expm(A*tt)%*%x0)
>>  pred<-sapply(t,sol)
>> }
>>
>> Mpred <- function(theta) {
>> # WARNING: assumes t global
>> kvec<-exp(theta[1:3])
>> k1<-kvec
>> k2<-kvec
>> k3<-kvec
>> #   MIN problem terbuthylazene disappearance
>>    z<-k1+k2+k3
>>    y<-z*z-4*k1*k3
>>    l1<-0.5*(-z+sqrt(y))
>>    l2<-0.5*(-z-sqrt(y))
>>    val<-100*(1-((k1+k2+l2)*exp(l2*t)-(k1+k2+l1)*exp(l1*t))/(l2-l1))
>> } # val should be a vector if t is a vector
>>
>> negll <- function(theta){
>> # non expm version JN 110731
>>  pred<-Mpred(theta)
>>  sigma<-exp(theta)
>>  -sum(dnorm(dat[,2],mean=pred,sd=sigma, log=TRUE))
>> }
>>
>> theta<-rep(-2,4)
>> fand<-nlogL(theta)
>> fsim<-negll(theta)
>> cat("Check fn vals: expm =",fand,"   simple=",fsim,"  diff=",fand-fsim,"\n")
>>
>> cat("time the function in expm form\n")
>> tnlogL<-microbenchmark(nlogL(theta), times=100L)
>> tnlogL
>>
>> cat("time the function in simpler form\n")
>> tnegll<-microbenchmark(negll(theta), times=100L)
>> tnegll
>>
>> ftimes<-data.frame(texpm=tnlogL\$time, tsimp=tnegll\$time)
>> # ftimes
>>
>>
>> boxplot(log(ftimes))
>> title("Log times in nanoseconds for matrix exponential and simple MIN fn")
>>
>
> --
> Charles C. Berry                         cberry at tajo.ucsd.edu
>
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--
Peter Dalgaard, Professor,
Center for Statistics, Copenhagen Business School
Solbjerg Plads 3, 2000 Frederiksberg, Denmark
Phone: (+45)38153501
Email: pd.mes at cbs.dk  Priv: PDalgd at gmail.com
"Døden skal tape!" --- Nordahl Grieg

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