This repository contains a collection of implementations of various numerical methods such as Newton-Raphson, backward Euler, and discrete Fourier transform.

This root-finding method iteratively moves towards the root of a function starting from an initial guess by using the derivative of the function. If the initial guess is real and the function itself maps reals to reals, then the roots found will only be restricted to the real domain. See here for more info: https://en.wikipedia.org/wiki/Newton%27s_method

This root-finding algorithm locates the roots of a function by interpolating a quadratic through three points of the given function and recursively approximating the root of the function using the roots of this quadratic. Since a quadratic function is used, this method can also find complex roots. See here for more info: https://en.wikipedia.org/wiki/Muller%27s_method

Forward Euler uses linear approximations of the solution to the differential equation at each point to predict the next point at the next step. This is a method of order 1. See here for more info: https://en.wikipedia.org/wiki/Euler_method

Backward Euler uses the evaluation of y' at the next step to compute the next value of y. This is a method of order 1. See here for more info: https://en.wikipedia.org/wiki/Backward_Euler_method

Runge-Kutta, in partiular the RK4 method, utilizes four different slopes to predict the next value of y at the next step. This is a fourth order method. See here for more info: https://en.wikipedia.org/wiki/Runge%E2%80%93Kutta_methods

DFT represents a discretized version of the Fourier integral and is often encoded in a unitary matrix. See here for more info: https://en.wikipedia.org/wiki/Discrete_Fourier_transform https://en.wikipedia.org/wiki/DFT_matrix

FFT refers to any methods of calculating DFT that have complexity $O(n log(n))$ rather than $O(n^2)$, as is typically standard for simpler DFT methods. The discrete_fourier_transform.m file contains an implementation of the Cooley-Tukey Algorithm. See here for more info: https://en.wikipedia.org/wiki/Fast_Fourier_transform https://en.wikipedia.org/wiki/Cooley%E2%80%93Tukey_FFT_algorithm

Gauss-Legendre Quadrature approximates an integral over the interval $[-1, 1]$ by approximating the integral as a weighted sum of function evaluations of the nth Legendre polynomial roots, where larger $n$ results in more accurate approximations. See here for more info: https://en.wikipedia.org/wiki/Gauss%E2%80%93Legendre_quadrature

Monte-Carlo integration refers to techniques which involve randomly sampling from the integration domain, computing the MLE for the expected value of the function, and then multiplying this mean by the volume of the subset. See here for more info: https://en.wikipedia.org/wiki/Monte_Carlo_integration