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  2. Ackermann function - Wikipedia

    en.wikipedia.org/wiki/Ackermann_function

    The inverse of the Ackermann function appears in some time complexity results. For instance, the disjoint-set data structure takes amortized time per operation proportional to the inverse Ackermann function, [24] and cannot be made faster within the cell-probe model of computational complexity. [25]

  3. Iterated logarithm - Wikipedia

    en.wikipedia.org/wiki/Iterated_logarithm

    The iterated logarithm is closely related to the generalized logarithm function used in symmetric level-index arithmetic. The additive persistence of a number , the number of times someone must replace the number by the sum of its digits before reaching its digital root , is O ( log ∗ ⁡ n ) {\displaystyle O(\log ^{*}n)} .

  4. Parallel algorithms for minimum spanning trees - Wikipedia

    en.wikipedia.org/wiki/Parallel_algorithms_for...

    Observe the graph that consists solely of edges collected in the previous step. These edges are directed away from the vertex to which they are the lightest incident edge. The resulting graph decomposes into multiple weakly connected components. The goal of this step is to assign to each vertex the component of which it is a part.

  5. Disjoint-set data structure - Wikipedia

    en.wikipedia.org/wiki/Disjoint-set_data_structure

    Here, the function () is the inverse Ackermann function. The inverse Ackermann function grows extraordinarily slowly, so this factor is 4 or less for any n that can actually be written in the physical universe. This makes disjoint-set operations practically amortized constant time.

  6. Tetration - Wikipedia

    en.wikipedia.org/wiki/Tetration

    of the infinitely iterated exponential converges for the bases () The function | (⁡) ⁡ | on the complex plane, showing the real-valued infinitely iterated exponential function (black curve) Tetration can be extended to infinite heights; i.e., for certain a and n values in n a {\displaystyle {}^{n}a} , there exists a well defined result for ...

  7. Kruskal's algorithm - Wikipedia

    en.wikipedia.org/wiki/Kruskal's_algorithm

    These operations take amortized time O(α(V)) time per operation, giving worst-case total time O(E α(V)) for this loop, where α is the extremely slowly growing inverse Ackermann function. This part of the time bound is much smaller than the time for the sorting step, so the total time for the algorithm can be simplified to the time for the ...

  8. Davenport–Schinzel sequence - Wikipedia

    en.wikipedia.org/wiki/Davenport–Schinzel_sequence

    The best bounds known on λ s involve the inverse Ackermann function. α(n) = min { m | A(m,m) ≥ n}, where A is the Ackermann function. Due to the very rapid growth of the Ackermann function, its inverse α grows very slowly, and is at most four for problems of any practical size. [3] Using big O and big Θ notation, the following bounds are ...

  9. Inverse Ackermann function - Wikipedia

    en.wikipedia.org/?title=Inverse_Ackermann...

    Pages for logged out editors learn more. Contributions; Talk; Inverse Ackermann function