## Overview

In Python, the Fibonacci series is a mathematical sequence that begins with 0 and 1 and continues with each new number equal to the sum of the two numbers that came before it. Generating the Fibonacci sequence in Python is a fantastic approach to practice recursion and iterative solutions, in addition to being a classic programming exercise.

F(0) = 0

F(1) = 1

F(n) = F(n-1) + F(n-2) for n > 1

Read More: fibonacci series in python using recursion

## The Fibonacci Series: What Is It?

Every number in the Fibonacci series is equal to the sum of the two numbers that came before it, starting with 0 and 1.

## Memoization for Efficient Calculation

Memoization is a technique that speeds up algorithms or computer programs by caching the output of costly function calls and returning it when the same inputs are entered again. It is helpful for maximizing Fibonacci computations since the recursive method generates inefficiencies by repeatedly calculating the same Fibonacci values.

## How Memoization Reduces Redundant Calculations

Without memoization, the recursive mechanism in Fibonacci computations repeatedly calculates the same numbers.This is resolved by memorization, which stores the outcomes. The function uses the problem’s determined outcome when it is called again with the same input.

A technique called dynamic programming divides a problem into smaller subproblems, fixes each subproblem only once, and stores the answers to prevent repeating calculations. This method works very well for resolving difficult issues, such as correctly computing Fibonacci numbers.

An explanation of the Fibonacci approach using dynamic programming is as follows: Fibonacci numbers are computed and then stored in an array or dictionary for further usage. Dynamic programming saves the Fibonacci numbers once and retrieves them as needed, as opposed to recalculating them.

The intermediate Fibonacci numbers can be stored using the dynamic programming technique using an array or a dictionary (hash table).

## The advantages of dynamic programming with respect to temporal complexity

When it comes to temporal complexity, the dynamic programming approach to Fibonacci number calculation offers the following benefits:

Decreased Time Complexity: Fibonacci computations using dynamic programming have a time complexity of O(n) instead of O(2^n) as in the naïve recursive method.

Effective Reuse: Dynamic programming prevents unnecessary computations by saving interim results. To increase performance, each Fibonacci number is only computed once and then fetched from memory as needed.

Enhanced Scalability: The dynamic programming approach is suitable for real-world applications since it maintains its efficiency even for large values of “n.”

## Maximizing Space for Fibonacci

In order to minimize memory use, space optimization techniques for computing Fibonacci numbers save only the crucial prior values rather than the complete series. When memory efficiency is an issue, these methods are quite helpful.

## Using Variables to Hold Just the Essential Past Values

Using variables to hold only the last two Fibonacci numbers instead of an array to record the entire sequence is one of the most popular space-optimized Fibonacci techniques.