Input sizes vs time complexity 1. n ≤ 10: Expected Time Complexity: Factorial or high-base exponential (e.g., O(n² ⋅ n!) or O(4ⁿ)). Approach: Backtracking or brute-force recursive algorithms. 2. 10 < n ≤ 20: Expected Time Complexity: Exponential, typically O(2ⁿ). Approach: Consider all subsets/subsequences, using backtracking and recursion. 3. 20 < n ≤ 100: Expected Time Complexity: Cubic, up to O(n³). Approach: Nested loops, brute-force solutions. Optimize with hash maps or heaps. 4. 100 < n ≤ 1,000: Expected Time Complexity: Quadratic, typically O(n²). Approach: Nested loops. Often the optimal solution in this range. 5. 1,000 < n ≤ 100,000: Expected Time Complexity: Log-linear, O(n log n) or linear, O(n). Approach: Sorting, heaps, or two-pointer techniques. Avoid nested loops 6. 100,000 < n ≤ 1,000,000: Expected Time Complexity: Linear, O(n) or log-linear, O(n log n) with small constants. Approach: Hash maps, efficient data structures, and algorithms. 7. n > 1,000,000: Expected Time Complexity: Logarithmic, O(log n) or constant, O(1). Approach: Binary search, clever use of hash maps, and mathematical tricks. Follow Tushar Verma for more such content: #Coding #TimeComplexity #Algorithms #SoftwareDevelopment #BigO #Efficiency #TechTips