This repository contains a comprehensive Jupyter notebook designed as an examination resource for a course in Numerical Computing. The notebook includes detailed explanations, examples, and exercises on various topics essential for understanding and applying numerical methods to solve mathematical problems.
Matrix Multiplication: Explains the theory and implementation of matrix multiplication, including examples and exercises. Determinants and Inverses: Discusses methods to calculate the determinant and inverse of a matrix, with practical code implementations.
Eigenvalues and Eigenvectors: Covers the computation of eigenvalues and eigenvectors, with detailed explanations and examples.
Root-Finding Algorithms: Detailed explanations and implementations of algorithms such as Newton-Raphson and bisection methods.
Interpolation: Discusses various interpolation techniques, including linear and polynomial interpolation, with practical examples.
Numerical Integration: Covers methods like trapezoidal rule and Simpson's rule, with step-by-step implementations and exercises.
Differential Equations: Explains numerical solutions for ordinary differential equations (ODEs) using methods such as Euler's method and Runge-Kutta methods.