algo-complexity-analysis

name	algo-complexity-analysis
description	Derive Big O time/space complexity of loops and recursion via recurrence relations; simplify expressions, compare growth at scale. Use when asked the complexity of code, hunting O(n^2) loops, or worst-case cost.
tool_type	python
primary_tool	Python

Algorithmic Complexity Analysis (Big O)

When to Use

• Asked to state or derive the time/space complexity ("what's the Big O of this function") of a code snippet or algorithm
• Reviewing code for hidden quadratic behavior: in on a list inside a loop, += string concatenation, sorted() inside a loop
• Choosing between data structures/algorithms for scale (list vs set membership, sort vs heap)
• Deriving recursive complexity via a recurrence relation, e.g. T(n) = 2T(n/2) + n (Master theorem)
• Explaining best/average/worst-case or amortized cost (list.append, hash table lookup, quicksort pivot choice)

Version Compatibility

• Language-agnostic technique; example code uses Python ≥3.8 stdlib only (math, no third-party dependencies)

Prerequisites

• Comfortable reading Python loops, function calls, and recursion
• No packages to install

Complexity Classes (fastest to slowest)

Class	Name	Example
O(1)	Constant	Dict lookup, array index
O(log n)	Logarithmic	Binary search
O(n)	Linear	Linear scan, single pass
O(n log n)	Linearithmic	Merge sort, heap sort
O(n²)	Quadratic	Nested loops, bubble sort
O(2ⁿ)	Exponential	Naive recursive Fibonacci, subset enumeration
O(n!)	Factorial	Permutation enumeration, brute-force TSP

Simplification Rules

O(2n + 5)    →  O(n)      # drop constants
O(n² + n)    →  O(n²)     # drop lower-order terms
O(500)       →  O(1)
O(n² + n³)   →  O(n³)
O(A) then O(B)  →  O(A + B)   # sequential loops add
O(A) inside O(B) →  O(A × B)  # nested loops multiply

Goal: classify the complexity of straight-line and loop-based code. Approach: identify the loop structure (single / nested / sequential / halving), express operation count as a function of n, then apply the simplification rules above.

def get_first_element(arr):
    """Return the first element. O(1) time, O(1) space.

    Direct index access: base_address + index * element_size,
    independent of array length.
    """
    return arr[0] if arr else None


def binary_search(arr, target):
    """Find target in a sorted array. O(log n) time, O(1) space.

    Each iteration halves the search space: after k steps the
    remaining size is n / 2**k, so it terminates in ~log2(n) steps.
    """
    left, right = 0, len(arr) - 1
    while left <= right:
        mid = (left + right) // 2       # O(1)
        if arr[mid] == target:
            return mid
        elif arr[mid] < target:
            left = mid + 1               # discard left half
        else:
            right = mid - 1              # discard right half
    return -1


def has_duplicate_naive(arr):
    """O(n²) time, O(1) space: compares every pair of elements."""
    n = len(arr)
    for i in range(n):
        for j in range(i + 1, n):
            if arr[i] == arr[j]:
                return True
    return False


def has_duplicate_fast(arr):
    """O(n) time, O(n) space: trades memory for speed via a set."""
    seen = set()
    for x in arr:
        if x in seen:        # O(1) average membership test
            return True
        seen.add(x)
    return False

Goal: derive complexity of recursive functions. Approach: write the recurrence relation T(n) = a*T(n/b) + f(n) (Master theorem form), then match it to a known pattern.

def merge_sort(arr):
    """Sort via divide-and-conquer. O(n log n) time, O(n) space.

    Recurrence: T(n) = 2*T(n/2) + O(n)  ->  O(n log n)
    (log n levels of recursion, O(n) merge work per level)
    """
    if len(arr) <= 1:
        return arr
    mid = len(arr) // 2
    left = merge_sort(arr[:mid])
    right = merge_sort(arr[mid:])
    return _merge(left, right)


def _merge(left, right):
    """Merge two sorted lists. O(n) time, O(n) space."""
    result, i, j = [], 0, 0
    while i < len(left) and j < len(right):
        if left[i] <= right[j]:
            result.append(left[i]); i += 1
        else:
            result.append(right[j]); j += 1
    result.extend(left[i:])
    result.extend(right[j:])
    return result


def fibonacci_naive(n):
    """Naive recursive Fibonacci. O(2^n) time, O(n) space (call stack).

    Recurrence: T(n) = T(n-1) + T(n-2) + O(1)  ->  O(2^n)
    Each call branches into two more calls, forming a binary call tree.
    """
    if n <= 1:
        return n
    return fibonacci_naive(n - 1) + fibonacci_naive(n - 2)

Pattern	Recurrence	Solution
Linear recursion	T(n) = T(n-1) + 1	O(n)
Binary recursion (no reuse)	T(n) = 2T(n-1) + 1	O(2ⁿ)
Divide & conquer (merge sort)	T(n) = 2T(n/2) + n	O(n log n)
Binary search	T(n) = T(n/2) + 1	O(log n)

At Scale (n = 1,000,000)

Complexity	Operations	Feasible?
O(1)	1	Yes
O(log n)	~20	Yes
O(n)	1,000,000	Yes
O(n log n)	~20,000,000	Yes
O(n²)	10¹²	No
O(2ⁿ)	∞	Never

Best / Average / Worst

Algorithm	Best	Average	Worst
Binary search	O(1)	O(log n)	O(log n)
Quicksort	O(n log n)	O(n log n)	O(n²)
Merge sort	O(n log n)	O(n log n)	O(n log n)
Hash table lookup	O(1)	O(1)	O(n)
BFS/DFS	O(V+E)	O(V+E)	O(V+E)

Amortized Complexity

• Python list.append(): O(1) amortized (occasional O(n) resize when capacity doubles, but rare)
• Python dict/set lookup: O(1) average; O(n) worst case (pathological hash collisions — rare with good hashing)

Pitfalls

• Hidden O(n) inside a loop: x in some_list is O(n); inside an O(n) loop that's O(n²). Use a set for O(1) membership (see has_duplicate_fast above).
• String concatenation: s += x in a loop reallocates and copies each time → O(n²) total. Use ''.join(parts).
• sorted() is O(n log n): calling it inside a loop makes the loop O(n² log n) — sort once outside the loop.
• Recursion depth: unbounded recursion on large n hits Python's default ~1000-frame limit. Prefer an iterative rewrite over raising sys.setrecursionlimit.
• Space vs time trade-off: memoization/hash sets trade O(n) space for O(n²) → O(n) or O(1) repeated lookups.
• Worst-case vs average-case: quicksort degrades to O(n²) on already-sorted input with a naive pivot; use random/median-of-three pivot selection or Python's built-in Timsort (sorted()/list.sort()), which is O(n log n) worst case.

See Also

• algo-basic-algorithms — foundational search/sort algorithms referenced above
• algo-linear-binary-search — linear vs binary search trade-offs in depth
• algo-comparison-sorts — merge sort, quicksort, heapsort implementations
• algo-intro-memoization — turning exponential recursion into polynomial time