Calculus of Variations: Gradient Young Theorem

Explains the Wronskian and its role in determining linear independence of solutions to differential equations.

Covers optimization methods, convergence guarantees, trade-offs, and variance reduction techniques in numerical optimization.

Covers the Lipschitz gradient theorem and its applications in function optimization.

Explores the classical methods for solving optimization problems, emphasizing the Euler-Lagrange equation and its variants.

Discusses optimization techniques, focusing on local and global extrema in functions.