I had a look at NestList in Mathematica and there is nothing out of
the box to compute
[x, f(x), f(f(x)), f(f(f(x))), ...]
in Python. But still you can do the following one line program
sage: f(x) = 3.9*x*(1-x)
sage: orbit = [0.3]
sage: for _ in range(10): orbit.append(f(orbit[-1]))
sage: print
On Thu, 18 Sep 2014 at 08:20AM +0200, Vincent Delecroix wrote:
I had a look at NestList in Mathematica and there is nothing out of
the box to compute
[x, f(x), f(f(x)), f(f(f(x))), ...]
in Python. But still you can do the following one line program
I have a utility function that I use often
On Thursday, September 18, 2014 3:59:47 PM UTC+2, Dan Drake wrote:
So the above list is
[applyntimes(f, x, n) for n in range(whatever)]
... it works, but doesn't it call f way too often? Personally, I think the
for-loop with list appending is the easiest. The yield/list approach is the
Could anybody offer specific advice on what cpu to buy for a symbolic math
server? It will run Sage, Mathematica, and python code. We'll be using it
to do theoretical physics.
Our current machine is about 4 years old. It cost about $4K at the time.
Its specs are:
1U server
two
Could anyone tell me what I am doing wrong here?
sage: g(x,y) = ((x-y)/sqrt(1-(x-y)^2)/y)
sage: forget()
sage: var(alpha)
sage: assume(alpha0)
sage: assume(alpha1)
sage: assume(x-1+alpha)
sage: *assume(x-alpha-10)*
sage: g(x,y).integral(y, alpha, x+1, algorithm=maxima)
And yet I still get
