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Home»Math»Decomposing a factorial into large factors (second version)
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Decomposing a factorial into large factors (second version)

adminBy adminJune 9, 2025No Comments2 Mins Read1 Views
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Boris Alexeev, Evan Conway, Matthieu Rosenfeld, Andrew Sutherland, Markus Uhr, Kevin Ventullo, and I have uploaded to the arXiv a second version of our paper “Decomposing a factorial into large factors“. This is a completely rewritten and expanded version of a previous paper of the same name. Thanks to many additional theoretical and numerical contributors from the other coauthors, we now have much more precise control on the main quantity {t(N)} studied in this paper, allowing us to settle all the previous conjectures about this quantity in the literature.

As discussed in the previous post, {t(N)} denotes the largest integer {t} such that the factorial {N!} can be expressed as a product of {N} factors, each of which is at least {t}. Computing {t(N)} is a special case of the bin covering problem, which is known to be NP-hard in general; and prior to our work, {t(N)} was only computed for {N \leq 599}; we have been able to compute {t(N)} for all {N \leq 10000}. In fact, we can get surprisingly sharp upper and lower bounds on {t(N)} for much larger {N}, with a precise asymptotic

\displaystyle \frac{t(N)}{N} = \frac{1}{e} - \frac{c_0}{\log N} - \frac{O(1)}{\log^{1+c} N}

for an explicit constant {c_0 = 0.30441901\dots}, which we conjecture to be improvable to

\displaystyle \frac{t(N)}{N} = \frac{1}{e} - \frac{c_0}{\log N} - \frac{c_1+o(1)}{\log^{2} N}

for an explicit constant {c_1 = 0.75554808\dots}: … For instance, we can demonstrate numerically that

\displaystyle 0 \leq t(9 \times 10^8) - 316560601 \leq 113.

As a consequence of this precision, we can verify several conjectures of Guy and Selfridge, namely

Guy and Selfridge also claimed that one can establish {t(N) \geq N/4} for all large {N} purely by rearranging factors of {2} and {3} from the standard factorization {1 \times 2 \times \dots \times N} of {N!}, but surprisingly we found that this claim (barely) fails for all {N > 26244}:

The accuracy of our bounds comes from several techniques:

To me, the biggest surprise was just how stunningly accurate the linear programming methods were; the very large number of repeated prime factors here actually make this discrete problem behave rather like a continuous one.



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