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 relations between events, disjunctive constraints, and modeling with binary variables in optimization problems.
Covers the concept of weighted graphs and the Greedy algorithm for finding a minimal spanning tree.
Introduces the concept of shortest path, discussing weighted paths, Hamiltonian paths, and path optimization algorithms.
Explains the concept of trees in graph theory and the definition of a spanning tree.
Explores maximizing diversity in document selection, graph clique determination, theorems on negative type, and convex optimization.
Explores dynamic programming for the Knapsack problem, discussing strategies, algorithms, NP-hardness, and time complexity analysis.
Covers the problem statement of assembly, precision requirements, common couplings, stability, and spatial vectors.
Explores knowledge inference for graphs, discussing label propagation, optimization objectives, and probabilistic behavior.
