Lectures related to Linear Optimization: Directional Derivatives

Covers matrix kernels, images, linear applications, independence, and bases in vector spaces.

Explores linear independence in vector spaces and the concept of bases.

Covers unsupervised learning with PCA and K-means for dimensionality reduction and data clustering.

Covers approximation algorithms for optimization problems, LP relaxation, and randomized rounding techniques.

Covers the solutions of homogeneous linear differential equations and how to find linearly independent solutions.

Introduces the Simplex Method for optimal decision making in linear programming, covering basic and advanced concepts.