Lectures related to Iterative Methods for Linear Equations

Convex Optimization: Gradient Algorithms

Covers convex optimization problems and gradient-based algorithms to find the global minimum.

Explores linear systems, convergence, and solving methods with a focus on CPU time and memory requirements.

Explores Richardson's methods for iterative linear system solutions and error control in symmetric positive definite systems.

Method of Undetermined Coefficients

Explores the method of undetermined coefficients for solving non-homogeneous linear differential equations with constant coefficients.

Solving Equations with Newton's Method

Introduces Newton's method for solving non-linear equations iteratively, highlighting its fast convergence but also its potential failure to converge in some cases.

Numerical Analysis: Introduction to Computational Methods

Covers the basics of numerical analysis and computational methods using Python, focusing on algorithms and practical applications in mathematics.

Newton-Krylov-hookstep Methods

Covers Newton-Krylov-hookstep methods for finding roots of high-dimensional systems.