Lectures related to Error Analysis and Stability in Numerical Methods

Explores backward and central differences for numerical differentiation, analyzing their properties and error analysis.

Covers exercises related to numerical methods, focusing on computational algorithms and problem-solving strategies.

Covers the formalism and bases of dynamic systems, including differential equations and non-linear systems.

Explores numerical computing stability, error analysis, and truncation error impact.

Explores the concepts of linear and quadratic convergence rates, highlighting their differences and applications in numerical methods.

Discusses the bisection method for solving nonlinear equations and its implementation using Python with NumPy and Matplotlib.

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