Covers approximation algorithms for optimization problems, LP relaxation, and randomized rounding techniques.
Covers methods for solving nonlinear optimization problems, including direct search, Newton-Raphson, and branch and bound.
Explores optimization in energy system modeling, covering decision variables, objective functions, and different strategies with their pros and cons.
Explores the convexity of Lovász extension and submodular function maximization, focusing on extending functions to convex sets and proving their convexity.
Covers gradient line search methods and optimization techniques with an emphasis on Wolfe conditions and positive definiteness.
Covers the concept of equivalent formulation in constrained optimization and explores the tangent cone.
