Discrete Optimization and Scalability for Data Science

Catalog description

Topics covered include computational complexity, NP-hardness, data as networks, graph theoretic algorithms, exact, approximation, heuristic and online algorithms, and connections between convex and non-convex optimization problems. The theory and algorithms are applied to data science problems arising in statistical machine learning, statistical clustering, design of experiments, observational studies, sampling, and variable selection. Applications may be motivated using data from social networks, search engines, the stock market and elections.