Covers approximation algorithms for optimization problems, LP relaxation, and randomized rounding techniques.
Covers the Branch & Bound algorithm for efficient exploration of feasible solutions and discusses LP relaxation, portfolio optimization, Nonlinear Programming, and various optimization problems.
Explores the Branch and Bound algorithm in discrete optimization, efficiently finding optimal solutions by calculating lower bounds on subsets.
Explores optimization in energy system modeling, covering decision variables, objective functions, and different strategies with their pros and cons.
Covers optimization problems, minimal packing, and bounds in Integer Optimization.
