Numerical Algorithms with Python

Numerical Algorithms header

In the vast world of numerical algorithms, we dive into concepts and techniques aimed at manipulating and generating numbers. Within numerical algorithms, you can find solutions for problems such as interpolation, solving differential equations, numerical optimization, and more. In this section, we will explore numerical algorithms using the Python programming language with a series of in-depth articles.

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Number Generation

Fibonacci: An Intriguing Number Sequence

Fibonacci Series three algorithms


Fibonacci Series

The Fibonacci sequence is a sequence of numbers in which each term is the sum of the two previous ones: 0, 1, 1, 2, 3, 5, 8, 13, …We will explore different approaches to generate the Fibonacci sequence, including iterative ones and recursive. We will discuss the time complexities of each method and the situations where one outperforms the other.

Prime Numbers: the Magic of Arithmetic

Prime numbers are natural numbers greater than 1 that are divisible only by 1 and themselves. Over the course of several articles we will explore the importance of prime numbers and their applications in cryptography and optimization algorithms. In the in-depth articles we will present algorithms for generating prime numbers, such as the Sieve of Eratosthenes and the Miller-Rabin primality test. We will analyze the complexity of these algorithms and discuss their practical applications.

Generating Prime Numbers


Generating Prime Numbers

Factorization Algorithms

We will explore factoring algorithms, such as Pollard’s Rho Algorithm, which factor a number into prime factors. We will discuss the importance of such algorithms in modern cryptography.

Pollard’s Rho Factorization Algorithm

Interpolation algorithms

Let’s explore polynomial interpolation, an approach to approximating a function through a polynomial that passes through a given set of points.

Polynomial interpolation

Solving Differential Equations (ODE)

Euler’s method

The Euler method is an algorithm for solving ordinary differential equations (ODE). We will analyze its practical implementation and its limitations.

Euler’s method

Runge-Kutta method

The Runge-Kutta method is a more sophisticated class of algorithms for numerically solving ODEs, with higher accuracy than the Euler method.

The Runge-Kutta method

Numerical Optimization

Newton’s Optimization Algorithm

Newton’s numerical optimization

We will explore Newton’s optimization algorithm, used to find the minima or maxima of functions.

Conjugate Gradient Algorithm

Conjugate gradient is a numerical optimization algorithm used to solve systems of linear equations and optimization problems.

The Conjugate Gradient

Numerical Analysis

Finite Element Method

Il metodo degli elementi finiti è ampiamente utilizzato per risolvere equazioni differenziali parziali approssimando il dominio del problema con elementi discreti.

The finite element method

Newton-Raphson method: Roots approximation

The Newton-Raphson method is an algorithm for approximating the roots of a function. We will illustrate how to apply this method to find approximate solutions of nonlinear equations.

The Newton-Raphson method and the approximation of numerical roots

Monte Carlo simulations

We explore the concept of Monte Carlo simulations, used to estimate numerical outcomes using random sampling.

The Monte Carlo method and numerical simulations

Fourier Transform Algorithms

The FFT is an efficient algorithm for calculating the Fourier transform of a sequence of data, with applications in signal processing and other disciplines.

The Fourier Transform and related algorithms

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