Arithmetic to ensure positives
이전 댓글 표시
Hello all:
I am looking for a simple logic to ensure 'positive' for multiple variables. In pseudo code terms want to replace
if {(a-b>=0).and.(c-d>=0).and.(e-f>=0) then...}
without the use of boolean 'and' or special functions like max. Pure arithmetic will be much helpful.
My inspiration is
(a-b)^2 + (c-d)^2 + (e-f)^2 = 0
which uniquely ensures/enforces eqalities, a=b, c=d, e=f.
Any parallels.
Much appreciated.
Regards.
답변 (2개)
Walter Roberson
2015년 12월 27일
0 개 추천
This is not possible to do without at least one comparison, and comparisons are not pure arithmetic. Your inspiration (a-b)^2 + (c-d)^2 + (e-f)^2 = 0 involves a comparison and so is not pure arithmetic.
댓글 수: 5
Amit
2015년 12월 27일
Image Analyst
2015년 12월 27일
I can't think of any legitimate reason why someone would want to write a program that doesn't allow boolean relational operators like >= and &&. Aside from whether it's even possible, why would you want to torture yourself like that? So your scheme is fragile - make it non-fragile and get on with things.
John D'Errico
2015년 12월 27일
Note that your "target" idea,
(a-b)^2 + (c-d)^2 + (e-f)^2 = 0
is actually a terribly written thing in terms of numerical analysis. Sorry, but that is a bad thing to do when you might be working with floating point numbers.
As Image Analyst says, just write some basic code that does what you want instead of looking for some elegant (to you) code fragment that looks nice, but does bad things.
Walter Roberson
2015년 12월 27일
When you say that your optimization scheme is very fragile, are you talking about attempting to code constraints in a manner that is differentiable?
Amit
2016년 1월 3일
Walter Roberson
2016년 1월 4일
0 개 추천
(a-b)^2 + (c-d)^2 + (e-f)^2 = 0 is differentiable only because it is smoothly invertible, that it can be translated into a series of variable reductions. Inequalities cannot be inverted that way. You cannot even code a > 0 invertibly -- if you could then c>=d could be coded as (c-d)^2 - delta_c = 0 together with however you coded delta_c > 0.
Unless, that is, you are okay with coding Heaviside functions, in which delta_c > 0 translates to Heaviside(delta_c) - 1 = 0 after having defined Heaviside(0) as 0 (Heaviside(0) does not have a fixed value, not really; one of the common conventions says Heaviside(0) = 1/2).
But diff(Heaviside(delta_c),delta_c) is Dirac(delta_c) and that is considered a distribution rather than a particular value, definitely not continuously differentiable. I would not consider it suitable for the use in this situation, but perhaps the theory of GRG is more flexible than I am.
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