Covers approximation algorithms for optimization problems, LP relaxation, and randomized rounding techniques.
Explores optimization in energy system modeling, covering decision variables, objective functions, and different strategies with their pros and cons.
Explores Linear Programming Duality, covering weak duality, strong duality, Lagrange multipliers interpretation, and optimization constraints.
Explores dynamic programming for the Knapsack problem, discussing strategies, algorithms, NP-hardness, and time complexity analysis.
Covers optimization algorithms, convergence properties, and time complexity of sequences and functions.
Explores practical aspects of solving parity games, including winning strategies, algorithms, complexity, determinism, and heuristic approaches.
Explores greedy heuristics in optimization, integrality constraints, and comparison of optimization methods.
Covers the Branch & Bound algorithm for efficient exploration of feasible solutions and discusses LP relaxation, portfolio optimization, Nonlinear Programming, and various optimization problems.
