You are given a hypothetical, inefficient function that calculates the 'Nth Fibonacci number' using a naive recursive approach with exponential time complexity O(2^n). Your task is to provide a solution in Python that optimizes this calculation. Requirements: 1. Provide an implementation using 'Memoization' (Top-Down Dynamic Programming). 2. Provide an implementation using 'Tabulation' (Bottom-Up Dynamic Programming). 3. Provide an implementation using 'Matrix Exponentiation' to achieve O(log n) time complexity. 4. For each solution, analyze the Time Complexity (Big O) and Space Complexity. 5. Explain the trade-offs between readability, memory usage, and raw performance for each approach in the context of very large N (e.g., N > 1,000,000).