Explores maximizing diversity in document selection, graph clique determination, theorems on negative type, and convex optimization.
Explores network clustering, spectral clustering, k-means algorithm, eigenvalue properties, block model estimation, and structural similarity measurement.
Explores relations between events, disjunctive constraints, and modeling with binary variables in optimization problems.
Introduces induced flows, basis matrices, and tree solutions in graph theory.
Explores primal-dual optimization methods, focusing on Lagrangian approaches and various methods like penalty, augmented Lagrangian, and splitting techniques.
Covers the problem statement of assembly, precision requirements, common couplings, stability, and spatial vectors.
