Exponential search locates a search interval by doubling its size, then uses binary search..
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 | from bisect import bisect_left # Exponential Search in Python # # PURPOSE # Exponential search locates a search interval by doubling its size, then uses binary # search. # # METHOD # Check index zero. Double a bound until it passes the target or the input end, then binary- # search the bounded interval. # # COMPLEXITY # O(log(p+1)) comparisons for a match near index p; O(log n) worst case, O(1) space. # # CONTRACT # Ascending input; returns any matching index or -1. # Public APIs infer array lengths except in C, which accepts pointer/length pairs. Vertex # counts and matrix dimensions remain explicit. Arrays must contain signed 32-bit integers. # Sorted order, correct tree topology, and caller-owned data-structure invariants are # preconditions; checking them on every query would change query complexity. # Scalar and shape checks are included. Value-range checks are included where needed to # prevent invalid indexing or nontermination. Allocation sizes are capped at 4,000,000 # workspace entries. Arithmetic outside Python raises an error on 32-bit overflow; Python # uses arbitrary precision. Native allocation failures propagate. In-place outputs may be # partly updated after arithmetic failure. These are single-threaded, in-memory # implementations. # # LANGUAGE DETAILS # Python 3.9+: automatic lengths, native lists, arbitrary-precision arithmetic, and # bisect_left for LIS. Invalid scalar/shape inputs raise ValueError. Integer element types # and sorted/tree invariants remain caller preconditions. # # EXAMPLE # Expected return: 2. # Run: python example.py # Tests for this exact source artifact run before publication. The accompanying source # archive includes unit cases, runtime results, and source hashes. def alg_require(condition): if not condition: raise ValueError("invalid algorithm input; see contract") def alg_size(n): alg_require(0 <= n <= 4000000) return n def exponential_search(a: list[int], target: int) -> int: alg_require(isinstance(a, (list, tuple)) and len(a) <= 1000000) alg_require(type(target) is int and -2147483648 <= target <= 2147483647) n: int = len(a) alg_require(n >= 0 and n <= 1000000) alg_require(len(a) == n) if n == 0: return -1 if a[0] == target: return 0 bound: int = 1 while bound < n and a[bound] < target: if bound > n // 2: bound = n else: bound *= 2 low: int = bound // 2 high: int = min(bound, n - 1) mid: int = 0 while low <= high: mid = low + (high - low) // 2 if a[mid] == target: return mid if a[mid] < target: low = mid + 1 else: high = mid - 1 return -1 a = [1, 3, 5, 7] target = 5 result = exponential_search(a, target) print(result) |