Covers the problem statement of assembly, precision requirements, common couplings, stability, and spatial vectors.
Covers the proof of the Bourgain's ARV Theorem, focusing on the finite set of points in a semi-metric space and the application of the ARV algorithm to find the sparsest cut in a graph.
Explores Belief Propagation in graphical models, factor graphs, spin glass examples, Boltzmann distributions, and graph coloring properties.
Covers the definition of flow in a network and flow calculation.
Explores maximizing diversity in document selection, graph clique determination, theorems on negative type, and convex optimization.
Explores belief propagation on graphs, emphasizing normalization, recursive relations, and iterative computation of the partition function.
