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For many problems, the algorithm with the best asymptotic complexity has a very large constant factor that is hidden by big O notation. This occurs in matrix multiplication, integer multiplication (specifically, the recent O(n log n) integer multiplication algorithm of Harvey and van der Hoeven), low-depth sorting networks and finding graph minors, to make a few. Such algorithms are sometimes called Galactic algorithms.

Note that for other algorithms, such as general sorting and integer addition, algorithms are known with optimal asymptotic complexity and small constant factors.

What research has been done in separating the former algorithms from the latter algorithms, from a theoretical perspective?

I am aware that hidden constants are often omitted to hide the distinction between different models of computation. However, I am confident that under a wide variety of different models, these Galactic algorithms will be slower than asymptotically worse algorithms for inputs of size one billion, for instance. The distinction is not subtle, in some cases. Has it been made rigorous?

For instance, one could invent a very simple model of computation, such as a von Neumann machine with a very simple ISA, and then implement the algorithms and bound their running times with explicit constants. Has this been done for a variety of algorithms?

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    $\begingroup$ Fast integer multiplication algorithms are not galactic. They are actually commonly used in practice. $\endgroup$ – Emil Jeřábek Jul 11 at 6:36
  • $\begingroup$ @EmilJeřábek May be the OP is talking about the recent breakthrough of David Harvey and Joris van der Hoeven, "Integer multiplication in $O(n \log n)$ time", which is galactic (see this this entry of Lipton's blog for example: rjlipton.wordpress.com/2019/03/29/… ) $\endgroup$ – Lamine Jul 11 at 8:20
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    $\begingroup$ As the authors write themselves (and is mentioned on Lipton’s blog), the paper for simplicity does not try to optimize the constants, but they very likely can be made practical. $\endgroup$ – Emil Jeřábek Jul 11 at 8:28
  • $\begingroup$ @EmilJeřábek That paper was indeed the one I was talking about. The paper does describe improvements that could be made, but it is extremely doubtful that the algorithm as is will ever be a practical improvement over current O(n log n log log n) algorithms that are used in practice, given how small log log n is for practical inputs. $\endgroup$ – isaacg Jul 11 at 17:52
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    $\begingroup$ @EmilJeřábek Specifically, the algorithm presented in the paper defers to a simpler algorithm for a base case whenever the number has less than $2^{d^{12}}$ bits, where they currently take $d=1729$. The optimizations they describe might allow them to use $d=9$ instead, but $2^{9^{12}}$ bits still exceeds the number of particles in the universe, so practicality is still out of the question. See Section 5.4 of their paper. $\endgroup$ – isaacg Jul 11 at 17:59

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