Lectures related to Eigenvalues and Optimization: Numerical Analysis Techniques

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

Covers the basics of linear algebra, including matrix operations and singular value decomposition.

Explores eigenvalues, eigenvectors, and methods for solving linear systems with a focus on rounding errors and preconditioning matrices.

Explores the Jacobi method and diagonalization techniques, including similarity transformation, power methods, and QR decomposition.

Explores the complexity of matrix computations, focusing on eigenvalues and eigenvectors of symmetric matrices and the challenges in computing them.

Introduces ordinary differential equations, their order, numerical solutions, and practical applications in various scientific fields.

Discusses advanced analysis concepts, focusing on differential equations and timers in microcontrollers.