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Timeline for Hard-to-compute real numbers

Current License: CC BY-SA 3.0

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Oct 21, 2016 at 19:49 vote accept Mohammad Al-Turkistany
Oct 21, 2016 at 18:36 comment added Mohammad Al-Turkistany I think you answered the original question in my post.
Oct 21, 2016 at 17:44 comment added Noah Schweber @MohammadAl-Turkistany I'm not sure what "a polynomial bound on the number of incorrectly computed bits" means. We have one function, and one real - if the real is incomputable, the function gets infinitely many bits wrong: is that polynomial or not? (Are you asking something about the proportion of bits $f$ guesses correctly?)
Oct 21, 2016 at 17:02 comment added Mohammad Al-Turkistany Oops. I suggest a polynomial bound on the number of incorrectly computed bits. Is that still trivial?
Oct 21, 2016 at 16:58 comment added Noah Schweber @MohammadAl-Turkistany If you allow it to compute some bits incorrectly, then - as I wrote in my answer above - every real is easily computed. So you need to give some more restriction in order for that question to not be trivial.
Oct 21, 2016 at 16:57 comment added Mohammad Al-Turkistany I mean it may compute some of the bits incorrectly.
Oct 21, 2016 at 16:54 comment added Noah Schweber @MohammadAl-Turkistany What do you mean by "compute it's digits", if you're not asking to compute all the digits? As I wrote in my answer, you have to be very precise here, otherwise the question trivializes. Are you asking for a natural real with high (polytime) Kolmogorov complexity, or something else?
Oct 21, 2016 at 16:51 comment added Mohammad Al-Turkistany Are you aware of any natural real number that is very hard to compute its digits ( not necessarily all the digits)?
Oct 21, 2016 at 16:38 comment added Noah Schweber Note that this trick does not allow me to tell if a given TM runs in polynomial time: rather, it just gives me a list of new TMs $\psi_i$, each of which runs in polynomial time, such that every polytime $\varphi_m$ is equivalent to some $\psi_i$. It's these that I diagonalize against. Note that this same trick works for much broader classes - e.g. it lets me diagonalize against all the exponential-time computable functions.
Oct 21, 2016 at 16:37 comment added Noah Schweber @MohammadAl-Turkistany That's true - which is why that's not what I did. I listed all TMs, as well as all polynomials, and looked at "cut-off TMs": the $\langle i, j\rangle$th "cut-off TIM" is the TM you get by taking the $i$th TM and forcing it to halt in $p_j$-many steps (where $p_j$ is the $j$th polynomial). Recall the definition of $r$: "the $n$th bit of the binary expansion of $r$ is a $1$ iff $\varphi_i(n)$ does not halt and output $1$ in $\le p_j(n)$ steps". At no point do I need to know if a machine runs in polytime - I just look at the first few steps of the computation.
Oct 21, 2016 at 16:35 comment added Mohammad Al-Turkistany This post shows that deciding whether a TM runs in polynomial time is undecidable. So a list of all polynomial time computable TM's does not exist. Right.? cstheory.stackexchange.com/questions/5004/…
Oct 21, 2016 at 16:28 comment added Noah Schweber @MohammadAl-Turkistany I have edited my answer to comment on the different versions of the question you ask (see the beginning and end sections).
Oct 21, 2016 at 16:27 history edited Noah Schweber CC BY-SA 3.0
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Oct 21, 2016 at 15:05 comment added Noah Schweber @MohammadAl-Turkistany See my last paragraph before the line. No algorithm running in polynomial time correctly computes every bit of this real - in particular, if the $i$th algorithm runs in time bounded by the $j$th polynomial, then it gets the $\langle i, j\rangle$th bit wrong. This is an instance of a very general and powerful technique - diagonalization.
Oct 21, 2016 at 15:04 comment added Mohammad Al-Turkistany May be I am missing something but I don't see why your real number is hard to compute?
Oct 21, 2016 at 14:44 history answered Noah Schweber CC BY-SA 3.0