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Covers the fundamentals of graph theory, including vertices, edges, degrees, walks, connected graphs, cycles, and trees, with a focus on the number of edges in a tree.
Explores maximizing diversity in document selection, graph clique determination, theorems on negative type, and convex optimization.
Explores Two-Stage Stochastic Programs, problem reformulation, Benders decomposition, extreme points, and sensitivity analysis.
Introduces computational complexity, decision problems, quantum complexity, and probabilistic algorithms, including NP-hard and NP-complete problems.
