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Re: calculus inside a programming language
Posted: Sat Jun 06, 2009 12:47 pm
by whowhatwhere
NickJohnson wrote:Are you sure about that? I know it's possible to finitely approximate a limit to an arbitrary position, but I'm not sure there is a way to compute it precisely. From the stuff I've been reading it seems like you can't always do that and can't compute whether it's possible for a specific formula or not. I think calculus is not computable by a Turing machine.
Lambda Calculus != Turing Machine
Re: calculus inside a programming language
Posted: Sat Jun 06, 2009 12:59 pm
by NickJohnson
I'm not talking about lambda calculus, I'm talking about Newtonian calculus: series, derivatives, integrals etc. The issue is that normal calculus relies on the limit to derive and prove those things. Of course, once you have the derivative/integral formula for a function, you can just plug it in, but the idea is to have the computer derive it instead of you. Otherwise your calculus subroutines are just a bunch of edge cases.
And lambda calculus *is* computationally equivalent to the Turing machine. That's one of the major parts of the Church-Turing thesis, and why functional languages work.
Re: calculus inside a programming language
Posted: Sat Jun 06, 2009 2:53 pm
by whowhatwhere
NickJohnson wrote:I'm not talking about lambda calculus, I'm talking about Newtonian calculus: series, derivatives, integrals etc. The issue is that normal calculus relies on the limit to derive and prove those things. Of course, once you have the derivative/integral formula for a function, you can just plug it in, but the idea is to have the computer derive it instead of you. Otherwise your calculus subroutines are just a bunch of edge cases.
And lambda calculus *is* computationally equivalent to the Turing machine. That's one of the major parts of the Church-Turing thesis, and why functional languages work.
Okay, basically, mathematics in total is just a symbolic representation of a process. Since all of the mathematics is just manipulation of symbols following thought experiments (at base), they can be expressed as a manipulation of symbols just as easily. Thus, for instance, the Power Rule for derivatives (forgive the short Calc. lesson) is defined as
f(x) = nx^(n-1) or
(lambda (x) (* n (expt x (- n 1)))). Therefore, the derivative of
f(x) = 3x^2 or
(lambda (x) (* 3 (expt x 2))), is simplified
f(x) = 6x or
(lambda (x) (* x 6)). Again, this is just a simple example of symbolic manipulation. These manipulations are trivial, especially in a language as expressive as Lisp or Scheme. However, interpretation of these symbols is the complicated part which is left up to the person him/herself. Given infinite time and infinite space it's possible to interpret and compute the more complicated theories including limits., at least as far as I understand it. (Correct me if I am wrong, please.).
Re: calculus inside a programming language
Posted: Sun Jun 07, 2009 7:45 am
by Walling
syntropy wrote:Given infinite time and infinite space it's possible to interpret and compute the more complicated theories including limits., at least as far as I understand it. (Correct me if I am wrong, please.).
Given infinite time and infinite space you would be able to calculate anything... you would just never get the result (only after infinite time... which means never). A more serious question is whether, given infinite space (the tape on a Turing machine), you can calculate something in finite time, ie. the calculation ends and yields a result.
Whether that is the case for calculating limits in calculus, I simply don't know.
Re: calculus inside a programming language
Posted: Sun Jun 07, 2009 11:46 am
by ru2aqare
Walling wrote:Whether that is the case for calculating limits in calculus, I simply don't know.
I see two approaches to the problem. Either you can calculate the limits and somehow derive the derivate functions from that; or you can use an analytical approach and do some pattern matching (ie. derive(x^2) = 2x; derive(sin(x)) = cos(x); and so on). The problem with the first approach is that it's inexact, even if you could calculate the limits of an arbitrary function at an arbitrary x value. The problem with the second approach that there exist some functions which have no closed-form derivates (unfortunately I haven't touched analysis in quite a few years and I can't come up with an example, but I do remember that they exist), or the derivate of the function can't be interpreted everywhere (for example abs(x) is interpreted on R, while you can't calculate the limit/derivate of this function at x=0, because it just doesn't exist).
Either way, I see no practical use of it. Take a piece of paper and work out the limits by hand