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Fibonacci search has an average- and worst-case complexity of O(log n) (see Big O notation). The Fibonacci sequence has the property that a number is the sum of its two predecessors. Therefore the sequence can be computed by repeated addition. The ratio of two consecutive numbers approaches the Golden ratio, 1.618... Binary search works by ...
To encode an integer N: . Find the largest Fibonacci number equal to or less than N; subtract this number from N, keeping track of the remainder.; If the number subtracted was the i th Fibonacci number F(i), put a 1 in place i − 2 in the code word (counting the left most digit as place 0).
The usual Fibonacci numbers are a Fibonacci sequence of order 2. The cases n = 3 {\displaystyle n=3} and n = 4 {\displaystyle n=4} have been thoroughly investigated. The number of compositions of nonnegative integers into parts that are at most n {\displaystyle n} is a Fibonacci sequence of order n {\displaystyle n} .
A Lagged Fibonacci generator (LFG or sometimes LFib) is an example of a pseudorandom number generator. This class of random number generator is aimed at being an improvement on the 'standard' linear congruential generator. These are based on a generalisation of the Fibonacci sequence. The Fibonacci sequence may be described by the recurrence ...
The expansion produced by this method for a number is called the greedy Egyptian expansion, Sylvester expansion, or Fibonacci–Sylvester expansion of . However, the term Fibonacci expansion usually refers, not to this method, but to representation of integers as sums of Fibonacci numbers .
With the exceptions of 1, 8 and 144 (F 1 = F 2, F 6 and F 12) every Fibonacci number has a prime factor that is not a factor of any smaller Fibonacci number (Carmichael's theorem). [56] As a result, 8 and 144 ( F 6 and F 12 ) are the only Fibonacci numbers that are the product of other Fibonacci numbers.
Moreover, unlike a binary search tree, most of this space is being used to store data: even for billions of elements, the pointers in a full vEB tree number in the thousands. The implementation described above uses pointers and occupies a total space of O(M) = O(2 m), proportional to the size of the key universe. This can be seen as follows.
Canonical Huffman codes address these two issues by generating the codes in a clear standardized format; all the codes for a given length are assigned their values sequentially. This means that instead of storing the structure of the code tree for decompression only the lengths of the codes are required, reducing the size of the encoded data.