Introduces the Random-Subcube Model (RSM) for constraint satisfaction problems, exploring its structure, phase transitions, and variable freezing.
Explores degree of freedom analysis, redundancy, and data reconciliation in process modeling and optimization.
Covers the concept of point estimation in statistics, focusing on methods to estimate unknown parameters from a given sample.
Explores Belief Propagation on trees, discussing cavity marginals, message passing algorithms, and the computation of free entropy.
Covers optimization methods, focusing on gradient methods and line search techniques.
Covers optimization principles, including linear optimization, networks, and concrete research examples in transportation.
