In computer science, the Fibonacci search technique is a method of searching a sorted array using a divide and conquer algorithm that narrows down possible locations with the aid of Fibonacci numbers. Compared to binary search where the sorted array is divided into two equal-sized parts, one of which is examined further, Fibonacci search divides the array into two parts that have sizes that are consecutive Fibonacci numbers. On average, this leads to about 4% more comparisons to be executed, but it has the advantage that one only needs addition and subtraction to calculate the indices of the accessed array elements, while classical binary search needs bit-shift, division or multiplication, operations that were less common at the time Fibonacci search was first published. Fibonacci search has an average- and worst-case complexity of O(log n) (see Big O notation).
If the elements being searched have non-uniform access memory storage (i. e., the time needed to access a storage location varies depending on the location accessed), the Fibonacci search may have the advantage over binary search in slightly reducing the average time needed to access a storage location. If the machine executing the search has a direct mapped CPU cache, binary search may lead to more cache misses because the elements that are accessed often tend to gather in only a few cache lines; this is mitigated by splitting the array in parts that do not tend to be powers of two. If the data is stored on a magnetic tape where seek time depends on the current head position, a tradeoff between longer seek time and more comparisons may lead to a search algorithm that is skewed similarly to Fibonacci search.
Fibonacci search is derived from Golden section search, an algorithm by Jack Kiefer (1953) to search for the maximum or minimum of a unimodal function in an interval.