Covers the Branch & Bound algorithm for efficient exploration of feasible solutions and discusses LP relaxation, portfolio optimization, Nonlinear Programming, and various optimization problems.
Covers approximation algorithms for optimization problems, LP relaxation, and randomized rounding techniques.
Covers the basics of linear programming, defining corners, extreme points, and feasible solutions within polyhedrons.
Explores the Branch and Bound algorithm in discrete optimization, efficiently finding optimal solutions by calculating lower bounds on subsets.
Covers the concept of hedging for Linear Programs and the simplex method, focusing on minimizing costs and finding optimal solutions.
Explores optimization in energy system modeling, covering decision variables, objective functions, and different strategies with their pros and cons.
