
Ravi,

There has been a lot of chatter about this, and people don't seem to be
reading carefully.  Perhaps this will help clarify things.

The problem appears to be that R was evaluating x^2 not as multiplication
x*x, but as x^2.0=exp(2.0*log(x)), using standard C functions for the
complex log and exponentiation.  Apparently the C library functions for
this have some inaccuracies in it, which showed up in your calculations.

One of the other responders said that matlab, octave, etc. detect the case
when there is a positive integer power and use multiplication to evaluate.
It appears that Martin Maechler has now submitted a change to R to detect
integer powers and evaluate them via repeated multiplication, which should
eliminate your problem.

There is no guarantee that R or matlab or any other program will evaluate
every expression correctly.  matlab has many years of use and evaluation
that have guided them in correct problems.  R is catching up, but not there
yet.  We are all part of improving R; your question led to a careful
examination of the issue and a fix within a few days.  No commercial
company responds so quickly!

HTH, John

...........................................................................

John P. Nolan
Math/Stat Department
227 Gray Hall
American University
4400 Massachusetts Avenue, NW
Washington, DC 20016-8050

jpnolan@american.edu
202.885.3140 voice
202.885.3155 fax
http://academic2.american.edu/~jpnolan
...........................................................................



-----r-devel-bounces@r-project.org wrote: -----


To: "'Martin Becker'" <martin.becker@mx.uni-saarland.de>
From: "Ravi Varadhan" <RVaradhan@jhmi.edu>
Sent by: r-devel-bounces@r-project.org
Date: 08/04/2009 10:59AM
cc: hwborchers@googlemail.com, r-devel@stat.math.ethz.ch
Subject: Re: [Rd] Inaccurate complex arithmetic of R (Matlab is accurate)

Please forgive me for my lack of understanding of IEEE floating-point
arithmetic.  I have a hard time undertsanding why "this is not a problem of
R itself", when "ALL" the other well known computing environments including
Matlab, Octave, S+, and Scilab provide accurate results.  My concern is not
really about the "overall" accuracy of R, but just about the complex
arithmetic.  Is there something idiosyncratic about the complex arithmetic?


I am really hoping that some one from the R core would speak up and address
this issue.  It would be a loss to the R users if this fascinating idea of
"complex-step derivative" could not be implemented in R.

Thanks,
Ravi.

----------------------------------------------------------------------------

-------

Ravi Varadhan, Ph.D.

Assistant Professor, The Center on Aging and Health

Division of Geriatric Medicine and Gerontology

Johns Hopkins University

Ph: (410) 502-2619

Fax: (410) 614-9625

Email: rvaradhan@jhmi.edu

Webpage:
http://www.jhsph.edu/agingandhealth/People/Faculty_personal_pages/Varadhan.h

tml



----------------------------------------------------------------------------

--------


-----Original Message-----
From: Martin Becker [mailto:martin.becker@mx.uni-saarland.de]
Sent: Tuesday, August 04, 2009 7:34 AM
To: Ravi Varadhan
Cc: r-devel@stat.math.ethz.ch; hwborchers@googlemail.com
Subject: Re: [Rd] Inaccurate complex arithmetic of R (Matlab is accurate)

Dear Ravi,

I suspect that, in general, you may be facing the limitations of machine
accuracy (more precisely, IEEE 754 arithmetics on [64-bit] doubles) in your
application. This is not a problem of R itself, but rather a problem of
standard arithmetics provided by underlying C compilers/CPUs.
In fact, every operation in IEEE arithmetics (so, this is not really a
problem only for complex numbers) may suffer from inexactness, a
particularly difficult one is addition/subtraction. Consider the following
example for real numbers (I know, it is not a very good one...):
The two functions

badfn <- function(x) 1-(1+x)*(1-x)
goodfn <- function(x) x^2

both calculate x^2 (theoretically, given perfect arithmetic). So, as you
want to allow the user to 'specify the mathematical function ... in "any"
form the user chooses', both functions should be ok.
But, unfortunately:

 > badfn(1e-8)
[1] 2.220446049250313e-16
 > goodfn(1e-8)
[1] 1e-16

I don't know what happens in matlab/octave/scilab for this example. They
may
do better, but probably at some cost (dropping IEEE arithmetic/do "clever"
calculations should result in massive speed penalties, try
evaluating   hypergeom([1,-99.9],[-49.9-24.9*I],(1+1.71*I)/2);   in
Maple...).
Now, you have some options:

- assume, that the user is aware of the numerical inexactness of ieee
arithmetics and that he is able to supply some "robust" version of the
mathematical function.
- use some other software (eg., matlab) for the critical calculations
(there
is a R <-> Matlab interface, see package R.matlab on CRAN), if you are
sure,
that this helps.
- implement/use multiple precision arithmetics within R (Martin Maechler's
Rmpfr package may be very useful:
http://r-forge.r-project.org/projects/rmpfr/ , but this will slow down
calculations considerably)

All in all, I think it is unfair just to blame R here. Of course, it would
be great if there was a simple trigger to turn on multiple precision
arithmetics in R. Packages such as Rmpfr may provide a good step in this
direction, since operator overloading via S4 classes allows for easy code
adaption. But Rmpfr is still declared "beta", and it relies on some
external
library, which could be problematic on Windows systems. Maybe someone else
has other/better suggestions, but I do not think that there is an easy
solution for the "general" problem.

Best wishes,

  Martin


Ravi Varadhan wrote:
> Dear Martin,
>
> Thank you for this useful trick.  However, we are interested in a
"general"
> approach for exact derivative computation.  This approach should allow
> the user to specify the mathematical function that needs to be
> differentiated in "any" form that the user chooses.  So, your trick
> will be difficult to implement there.  Furthermore, do we know for
> sure that `exponentiation' is the only operation that results in
> inaccuracy?  Are there other operations that also yield inaccurate
results
for complex arithmetic?
>
> Hans Borchers also checked the computations with other free numerical
> software, such as Octave, Scilab, Euler, and they all return exactly
> the same results as Matlab.  It would be a shame if R could not do the
same.
>
> It would be great if the R core could address the "fundamental" issue.
>
> Thank you.
>
> Best regards,
> Ravi.
>
> ----------------------------------------------------------------------
> ------
> -------
>
> Ravi Varadhan, Ph.D.
>
> Assistant Professor, The Center on Aging and Health
>
> Division of Geriatric Medicine and Gerontology
>
> Johns Hopkins University
>
> Ph: (410) 502-2619
>
> Fax: (410) 614-9625
>
> Email: rvaradhan@jhmi.edu
>
> Webpage:
> http://www.jhsph.edu/agingandhealth/People/Faculty_personal_pages/Vara
> dhan.h
> tml
>
>
>
> ----------------------------------------------------------------------
> ------
> --------
>
>
> -----Original Message-----
> From: Martin Becker [mailto:martin.becker@mx.uni-saarland.de]
> Sent: Monday, August 03, 2009 5:50 AM
> To: Ravi Varadhan
> Cc: r-devel@stat.math.ethz.ch; hwborchers@googlemail.com
> Subject: Re: [Rd] Inaccurate complex arithmetic of R (Matlab is
> accurate)
>
> Dear Ravi,
>
> the inaccuracy seems to creep in when powers are calculated.
> Apparently, some quite general function is called to calculate the
> squares, and one can avoid the error by reformulating the example as
follows:
>
> rosen <- function(x) {
>   n <- length(x)
>   x1 <- x[2:n]
>   x2 <- x[1:(n-1)]
>   sum(100*(x1-x2*x2)*(x1-x2*x2) + (1-x2)*(1-x2)) }
>
> x0 <- c(0.0094, 0.7146, 0.2179, 0.6883, 0.5757, 0.9549, 0.7136,
> 0.0849, 0.4147, 0.4540) h <- c(1.e-15*1i, 0, 0, 0, 0, 0, 0, 0, 0, 0)
> xh <- x0 + h
>
> rx <- rosen(xh)
> Re(rx)
> Im (rx)
>
>
> I don't know which arithmetics are involved in the application you
> mentioned, but writing some auxiliary function for the calculation of
> x^n when x is complex and n is (a not too large) integer may solve
> some of the numerical issues. A simple version is:
>
> powN <- function(x,n) sapply(x,function(x) prod(rep(x,n)))
>
> The corresponding summation in 'rosen' would then read:
>
> sum(100*powN(x1-powN(x2,2),2) + powN(1-x2,2))
>
>
> HTH,
>
>   Martin
>
>
> Ravi Varadhan wrote:
>
>> Dear All,
>>
>> Hans Borchers and I have been trying to compute "exact" derivatives
>> in R
>>
> using the idea of complex-step derivatives that Hans has proposed.
> This is a really, really cool idea.  It gives "exact" derivatives with
> only a minimal effort (same as that involved in computing first-order
> forward-difference derivative).
>
>> Unfortunately, we cannot implement this in R as the "complex arithmetic"
>>
> in R appears to be inaccurate.
>
>> Here is an example:
>>
>> #-- Classical Rosenbrock function in n variables rosen <- function(x)
>> { n <- length(x)
>> x1 <- x[2:n]
>> x2 <- x[1:(n-1)]
>> sum(100*(x1-x2^2)^2 + (1-x2)^2)
>> }
>>
>>
>> x0 <- c(0.0094, 0.7146, 0.2179, 0.6883, 0.5757, 0.9549, 0.7136,
>> 0.0849, 0.4147, 0.4540) h <- c(1.e-15*1i, 0, 0, 0, 0, 0, 0, 0, 0, 0)
>> xh <- x0 + h
>>
>> rx <- rosen(xh)
>> Re(rx)
>> Im (rx)
>>
>> #  rx = 190.3079796814885 - 12.13915588266717e-15 i  # incorrect
>> imaginary part in R
>>
>> However, the imaginary part of the above answer is inaccurate.  The
>>
> correct imaginary part (from Matlab) is:
>
>> 190.3079796814886 - 4.66776376640000e-15 i  # correct imaginary part
>> from Matlab
>>
>> This inaccuracy is serious enough to affect the acuracy of the
>> compex-step
>>
> gradient drastically.
>
>> Hans and I were wondering if there is a way to obtain accurate "small"
>>
> imaginary part for complex arithmetic.
>
>> I am using Windows XP operating system.
>>
>> Thanks for taking a look at this.
>>
>> Best regards,
>> Ravi.
>>
>>
>> ____________________________________________________________________
>>
>> Ravi Varadhan, Ph.D.
>> Assistant Professor,
>> Division of Geriatric Medicine and Gerontology School of Medicine
>> Johns Hopkins University
>>
>> Ph. (410) 502-2619
>> email: rvaradhan@jhmi.edu
>>
>> ______________________________________________
>> R-devel@r-project.org mailing list
>> https://stat.ethz.ch/mailman/listinfo/r-devel
>>
>>
>
>
> --
> Dr. Martin Becker
> Statistics and Econometrics
> Saarland University
> Campus C3 1, Room 206
> 66123 Saarbruecken
> Germany
>
>


--
Dr. Martin Becker
Statistics and Econometrics
Saarland University
Campus C3 1, Room 206
66123 Saarbruecken
Germany

______________________________________________
R-devel@r-project.org mailing list
https://stat.ethz.ch/mailman/listinfo/r-devel
	[[alternative HTML version deleted]]

