The solution still works out to a binary search in the sense that you'll need to partition the array into two parts to be examined.
In a sorted array, you just look at each part and determine whether the element lives in the first part (let's call this A) or the second part (B). Since, by the definition of a sorted array, partitions A and B will be sorted, this requires no more than some simple comparisons of the partition bounds and your search key.
In a rotated sorted array, only one of A and B can be guaranteed to be sorted. If the element lies within a part which is sorted, then the solution is simple: just perform the search as if you were doing a normal binary search. If, however, you must search an unsorted part, then just recursively call your search function on the non-sorted part.
This ends up giving on a time complexity of O(lg n)
.
(Realistically, I would think that such a data structure would have a index accompanying it to indicate how many positions the array has been rotated.)
Edit: A search on Google takes me to this somewhat dated (but correct) topic on CodeGuru discussing the same problem. To add to my answer, I will copy some pseudocode that was given there so that it appears here with my solution (the thinking follows the same lines):
Search(set):
if size of set is 1 and set[0] == item
return info on set[0]
divide the set into parts A and B
if A is sorted and the item is in the A's range
return Search(A)
if B is sorted and the item is in the B's range
return Search(B)
if A is not sorted
return Search(A)
if B is not sorted
return Search(B)
return "not found"
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