Optimized Fibonacci Calculation with Memoization

Code Analysis:

The provided code calculates the nth Fibonacci number using a recursive approach. However, the current implementation suffers from inefficiency due to redundant calculations, especially for larger values of 'n'.

Review of Previous Attempts:

The code snippet provided calculates the nth Fibonacci number but does so inefficiently due to redundant recursive calls.

Proposed Solution:

To improve the efficiency of the Fibonacci calculation, we can utilize memoization. Memoization involves storing the results of expensive function calls and returning the cached result when the same inputs occur again.

Code Development (Python):

# Implementing Fibonacci calculation using memoization
fibonacci_cache = {}  # Cache to store calculated Fibonacci values

def fibonacci(n):
    if n <= 0:
        return "Invalid input"
    elif n == 1:
        return 0
    elif n == 2:
        return 1
    elif n in fibonacci_cache:  # Check if value already calculated
        return fibonacci_cache[n]
    else:
        fibonacci_cache[n] = fibonacci(n-1) + fibonacci(n-2)
        return fibonacci_cache[n]

print(fibonacci(10))  # Example usage

Code Usage Example:

# Calling the fibonacci function to calculate the 10th Fibonacci number
print(fibonacci(10))  # Output: 34

By incorporating memoization, the Fibonacci function avoids redundant recalculations, significantly improving the performance, especially for larger values of 'n'.