Discrete Optimization: The Curse of Dimensionality

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Covers energy system modelling, optimization, scenarios, predictions, complexities, and controversies in energy models.

Explores linear programming optimization with constraints, Dijkstra's algorithm, and LP formulations for finding feasible solutions.

Covers classical optimization problems, brute force algorithms, and integer linear optimization.

Introduces the backpack problem, a discrete optimization problem with constraints and algorithms to solve it.

Covers the main idea behind the Simplex algorithm and explains the Tableau method for solving linear programming problems.

Explores dynamic programming for the Knapsack problem, discussing strategies, algorithms, NP-hardness, and time complexity analysis.

Covers the fundamentals of integer optimization, including integer programming, dynamic programming, and approximation algorithms.

Covers classical and quantum computational complexity concepts and implications.

Explores the knapsack problem and the traveling salesman problem with a focus on optimization algorithms.

Explores complexity classes P and NP, highlighting solvable and verifiable problems, including NP-complete challenges.