ChatGPT checks in on fractional iteration.
Explain fractional iteration.


Fractional iteration, also known as half-iteration or real iteration, is a concept in mathematics that refers to applying a function an arbitrary number of times, not just whole number times. For instance, applying a function twice is normal iteration, but what does it mean to apply a function 1.5 times, or even π times? This is the question that fractional iteration tries to answer.

While the idea of fractional iteration is simple to state, it's surprisingly deep and leads to a rich area of mathematics that touches on complex analysis, dynamical systems, and even chaos theory.

Let's take an example with the function f(x) = 2x. Applying it twice gives us f(f(x)) = f(2x) = 4x. But what would f^(1.5)(x) mean?

There's no universally accepted way to define fractional iteration, and many different approaches have been proposed. Some of these approaches only work for specific types of functions, while others are more general.

One common approach is to use a method known as the Schröder's equation. This approach involves finding a new function φ and a constant λ such that the iterates of f can be written in the form f^n(x) = φ^(-1)(λ^nφ(x)). Then, to find f^(t)(x) for a non-integer t, you just plug in t for n in this equation.

Another approach involves using the concept of a flow on a space. Here, you imagine the function as defining a kind of motion or flow on the space, and then "halfway" along this flow is interpreted as applying the function half of a time.

However, fractional iteration is generally a difficult problem and many aspects of it remain unsolved. For instance, for most functions we don't know if there is a way to define the half iterate that is continuous, differentiable, or satisfies other nice properties.

Are the composite functions of continuous and differentiable also continuous and differentiable?

Quote:Yes, the composition of continuous functions is always continuous, and the composition of differentiable functions is usually differentiable. Here's a more precise statement:

Continuity: If you have two functions f(x) and g(x), and both are continuous at a point a, then their composition function (f ◦ g)(x) = f(g(x)) is also continuous at a.

Differentiability: If you have two functions f(x) and g(x), and both are differentiable at a point a, then their composition function (f ◦ g)(x) = f(g(x)) is usually differentiable at a. However, there is an extra condition: the derivative of the inner function g at a (g'(a)) must exist, and the outer function f must be differentiable at the point g(a).

For example, let's say f(x) = x^2 and g(x) = sin(x). Both f(x) and g(x) are continuous and differentiable for all real numbers. So their composition (f ◦ g)(x) = (sin(x))^2 is also continuous and differentiable for all real numbers.

Please note that this only applies to points in the domain where the functions are defined and meet these conditions. For example, the function f(x) = 1/x is not continuous or differentiable at x = 0, so if g(x) = 0 for some x in its domain, the composition (f ◦ g)(x) will not be continuous or differentiable at those points.
ChatGPT  could also mistake the same two variables in Riemann sum's string for fractions or iteration. hyper-infinitely speaking, the part of the iteration gpt does well with is the irony that its easier than things like Gaussian methods but gpt could stand to lose a few variables itself, and mention the functional overload with proofs like that, the one with a Guassian metaphor a or b, i think it was could be something with a mulanept pattern or two and two Gaussians

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