Dear all,

I'm not a mathematician, so I am probably asking a very naive question here:

Could someone point me to a reason why anything should be printed as 
1.00000000*var? Those factors of 1.0000000 that turn up in otherwise 
pretty equations are among the most annoying 'features' to me. I would 
also appreciate a switch that would allow me to get rid of all decimal 
0s in the latex representation if they are not followed by a non-0. For 
example, I would like something like this:


1.2*x+1.000000002*y

instead of

1.20000000000000*x + 1.00000000020000*y


Thanks,
Stan

Alex Ghitza wrote:
> On Sun, Jul 19, 2009 at 9:04 AM, William Stein<[email protected]> wrote:
>   
>> Please do not make the above change.  It would be very inconsistent
>> with what happens for symbolic variables:
>>
>> sage: var('x,y,z,w')
>> sage: f = 1.0*x^2 - 1.0*y
>> sage: f.variables()
>> (x, y)
>>     
>
> I'm not sure symbolic variables are consistent to start with:
>
> sage: SR(1.0)
> 1.00000000000000
> sage: SR(1.0*x)
> x
> sage: SR(2.0*x)
> 2.00000000000000*x
> sage: SR(1.0*x+1.0)
> x + 1.00000000000000
>
>
> This does not look promising.  Printing should be consistent across
> similar elements of the same parent, and it's not.
>
> I would argue that, strictly speaking, the variable 'x' is not an
> element of the polynomial ring R[x].  The latter is defined as finite
> formal sums of the form
>
> a0 + a1*x + ... + an*x^n
>
> So x is not in R[x], but R(1)*x is.  x is a formal variable/symbol,
> while R(1)*x is a polynomial.
>
> What I'm trying to get at is this: we can't have our cake and eat it
> too.  If we agree that 1.0*x should be printed as 1.0000000000*x, and
> we want f.variables() to return x as an element of the polynomial
> ring, then it will be printed as 1.00000000000*x.
>
> If we make f.variables() return the formal variable x somehow, then we
> can always do R(x) to get the polynomial.
>
>
> I'm not saying that I think I have the correct solution to this, I'm
> just pointing out that symbolic expressions are printed almost as
> inconsistently as multivariate polynomials (and differently to
> univariate polynomials) so they might not be the right model to
> follow.
>
>
> Best,
> Alex
>
>
>
>   


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