RE: [Camllist] Efficient and canonical set representation?
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Date:   (:) 
From:  Brian Hurt <bhurt@s...> 
Subject:  Re: [Camllist] Rounding mode 
On Mon, 10 Nov 2003, Eric Dahlman wrote: > Christophe Raffalli wrote: > > > > > I saw a previous discussion about rounding mode for OCaml (in 2000). > > > > As anyone implemented the functions to change the rounding mode for > > floating point from OCaml (possibly as a patch to add primitives and > > save a C function call) ? > > > > This is necessary to implement interval arithmetic ... > > Somewhat off topic but why is this necessary from a numerical math type > of perspective. I am honestly curious as I don't see how this would > interact with the calculation in a meaningful way. > Simplistic discussion of why changing the rounding modes is usefull for numeric programming. If you know numerical analysis, stop reading now :). FP numbers are only precise to so many digits. So every time you do an operation, a small amount of error is introduced rather than being the exact answer, it's the closest number to the exact answer that FP numbers can represent. This is why the following code does not return the answer you expect: let unexpected () = let loop i n = if (i < 10) then loop (i + 1) (n +. 0.1) else n in loop 0 0. ;; 1/10 in binary is infinitely repeating the way 1/3 is in decimal. So the expression n +. 0.1 isn't adding *exactly* 0.1, but instead 0.1  e for some small e. Where e is ~1E16. But this is large enough that unexpected() returns 0.999999999999999889 and not 1.0. Note also that this is nothing to do with Ocaml C, Java, Perl, Python, FORTRAN, and every other language out there that does floating point has this problem it's a problem with floating point. Now, for a single operation this error is negligable one the order of 1e15 (assuming the answer is 1.0). But over large numbers of calculations, this error can accumulate, and become large enough to affect the results. Consider, for example, multiplying two numbers x and y. But you don't have x and y exactly, you have (x + e) and (y + e) for some error amount e. You multiply them, and you get (x + e)*(y + e) = x*y + x*e + y*e + e*e. Now, e*e is vanishingly small, we can ignore it. And, assuming x and y are both about 1.0, our answer is x*y + ~2*e in other words, we've about doubled the amount of error. Even starting at 1e15, doubling it every operation and you eat up all your precision surprisingly fast. How can we tell how much error we have? Well, you can either do a whole bunch of graduatelevel mathematics, or you can run the same algorithm with different rounding modes. If you always round towards positive infinity, you'll always be adding an error term i.e. it'll be (x + e) and (y + e) with a result of x*y + 2e. If you always round to negative infinity, you'll be subtracting the error term (x  e) and (y  e) with a result x*Y  2e. Comparing the results gives you both a good idea of where the answer really is, and how big your errors are. Note, all is not necessarily lost. If instead we have (x + e) and (y  e) a little bit of quick algebra shows that the result is actually *more* accurate than either of the original inputs. The errors tend to cancel instead of accumulate. Numerical analysts call algorithms whose errors tend to cancel "stable", while algorithsm whose errors tend to accumulate are "unstable". A good textbook on numerical methods will generally only discuss stable algorithms use 'em. The effect can also be sort of acheived by using a random rounding method. For each operation, the CPU picks one of it's rounding methods at random to use (well, pseudorandom anyways). Now, while this will in general increase the accuracy of floating point programs, it also means that every time you run a numerical program, even over the same data, you will get a slightly different answer. Newbies wondering why their program keeps changing it's answer will probably rival newbies wondering why their stack overflows pretty quickly. Which is why I'm hesitant to advocate doing this to all programs. I still would like to be able to select the rounding mode either at compile time or at run time, which ever is easier. Actually, run time is probably easier to implement and more usefull (I can switch rounding modes for different parts of the program).  "Usenet is like a herd of performing elephants with diarrhea  massive, difficult to redirect, aweinspiring, entertaining, and a source of mindboggling amounts of excrement when you least expect it."  Gene Spafford Brian  To unsubscribe, mail camllistrequest@inria.fr Archives: http://caml.inria.fr Bug reports: http://caml.inria.fr/bin/camlbugs FAQ: http://caml.inria.fr/FAQ/ Beginner's list: http://groups.yahoo.com/group/ocaml_beginners