Lectures related to Newton's local method: Geometric interpretation

Covers coordinate descent, a method for optimizing functions by updating one coordinate at a time.

Discusses numerical methods, focusing on stopping criteria, SciPy for optimization, and data visualization with Matplotlib.

Introduces optimization basics, covering logistic regression, derivatives, convex functions, gradient descent, and second-order methods.

Covers the derivation and application of Euler-Lagrange equations for optimization problems in mathematical analysis.

Covers optimization basics, including unconstrained optimization and gradient descent methods for finding optimal solutions.

Covers the KKT conditions for optimization with constraints, essential for solving constrained optimization problems efficiently.

Explores unconstrained optimization theory, covering global and local minima, convexity, and gradient concepts.