Lectures related to Variational Problems: Convexity and Coercivity

Covers linear programming, weighted bipartite matching, and vertex cover problems in optimization.

Covers implicit schemes in numerical analysis for solving partial differential equations.

Explores proof concepts, techniques, and applications in logic, mathematics, and algorithms.

Covers the basics of constrained optimization, including tangent directions, trust-region subproblems, and necessary optimality conditions.

Covers distributions, derivatives, convergence, and continuity criteria in function spaces.

Covers weak derivatives, their properties, and applications in functional analysis.

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

Covers gradient descent, its principles, applications, and convergence rates in optimization for machine learning.