 |
Center for Nonlinear Studies |
In many real-world problems, a big scale gap can be observed between micro- and macroscopic scales of the problem because of the difference in mathematical (engineering, social, biological, physical, etc.) models and/or laws at different scales. The main objective of multiscale algorithms is to create a hierarchy of problems, each representing the original problem at different coarse scales with fewer degrees of freedom. We will discuss different strategies of creating these hierarchies for large-scale discrete optimization and modeling problems related to network science. We will present in details frameworks for partitioning/clustering, compression, generation and epidemics response problems on networks.
Host: Aric Hagberg 5-4958