Integer Optimization: Theory and Applications

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Explores optimization in energy system modeling, covering decision variables, objective functions, and different strategies with their pros and cons.

Introduces algorithms as problem-solving procedures, covering complexity, correctness, and implementation in various languages.

Covers graphical models for probabilistic distributions using graphs, nodes, and edges.

Introduces greedy algorithms and matroids, highlighting their efficiency in solving optimization problems.

Covers coin rendering and the limitations of the greedy algorithm in finding optimal solutions.

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

Introduces an improved algorithm for three-color parity games, focusing on progress measures, acceleration, and practical speed-up.

Delves into the theory of computation, covering decidability, complexity, P vs. NP, and reductions.

Covers classical and quantum computational complexity concepts and implications.

Examines NP problems, graph coloring, path optimization, and computational complexity distinctions in P and NP classes.