Dynamic Programming: Integer Programming

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

Explores the cutset formulation for the MST Problem and Gomory Cutting Planes method.

Covers the process of formulating integer programs and improving solutions.

Explores convex polyhedra, linear programs, and their optimization importance.

Explores practical aspects of solving parity games, including winning strategies, algorithms, complexity, determinism, and heuristic approaches.

Delves into the complexity and interdependencies of transitioning to smart cities, emphasizing the importance of a holistic approach.

Explores algorithm correctness, worst-case complexity analysis, and efficiency comparison based on input size.

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

Covers approximation algorithms for optimization problems, LP relaxation, and randomized rounding techniques.

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