In computer science, the iterated logarithm of n, written log* n (usually read "log star"), is the number of times the logarithm function must be iteratively applied before the result is less than or equal to 1. The simplest formal definition is the result of this recursive function:
but on the negative real numbers, log-star is 0, whereas for positive x, so the two functions differ for negative arguments.
In computer science, lg* is often used to indicate the binary iterated logarithm, which iterates the binary logarithm instead. The iterated logarithm accepts any positive real number and yields an integer. Graphically, it can be understood as the number of "zig-zags" needed in Figure 1 to reach the interval [0, 1] on the x-axis.
Mathematically, the iterated logarithm is well-defined not only for base 2 and base e, but for any base greater than .
Analysis of algorithms
- Finding the Delaunay triangulation of a set of points knowing the Euclidean minimum spanning tree: randomized O(n log* n) time
- Fürer's algorithm for integer multiplication: O(n log n 2O(lg* n))
- Finding an approximate maximum (element at least as large as the median): lg* n − 4 to lg* n + 2 parallel operations
- Richard Cole and Uzi Vishkin's distributed algorithm for 3-coloring an n-cycle: O(log* n) synchronous communication rounds.
- Performing weighted quick-union with path compression 
The iterated logarithm grows at an extremely slow rate, much slower than the logarithm itself. For all values of n relevant to counting the running times of algorithms implemented in practice (i.e., n ≤ 265536, which is far more than estimated number of atoms in the known universe), the iterated logarithm with base 2 has a value no more than 5.
Higher bases give smaller iterated logarithms. Indeed, the only function commonly used in complexity theory that grows more slowly is the inverse Ackermann function.
The iterated logarithm is closely related to the generalized logarithm function used in symmetric level-index arithmetic. It is also proportional to the additive persistence of a number, the number of times one must replace the number by the sum of its digits before reaching its digital root.
- Olivier Devillers, "Randomization yields simple O(n log* n) algorithms for difficult ω(n) problems.". International Journal of Computational Geometry & Applications 2:01 (1992), pp. 97–111.
- Noga Alon and Yossi Azar, "Finding an Approximate Maximum". SIAM Journal of Computing 18:2 (1989), pp. 258–267.
- Richard Cole and Uzi Vishkin: "Deterministic coin tossing with applications to optimal parallel list ranking", Information and Control 70:1(1986), pp. 32–53.
- Cormen, Thomas H.; Leiserson, Charles E., Rivest, Ronald L. (1990). Introduction to Algorithms (1st ed.). MIT Press and McGraw-Hill. ISBN 0-262-03141-8. Section 30.5.
- On Separators, Segregators and Time versus Space