Description
Large complex systems can be modeled using the discoverable relationships and constraints amongst their derived big data sets. Engineers and decision makers may wish to identify designs and actions that yield optimal outcomes for those systems. The course will introduce the student to methods used to formulate and solve big data optimizations: linear, integer, and nonlinear systems, especially constrained systems. The course will include readings about cases studies and use several open source and commercial packages to solve representative problems.

Course Goals
The goals of this course include: 1) learn about big data analytics, including descriptive, diagnostic, predictive, and prescriptive data analytics, 2) learn about optimization programs to implement prescriptive analytics, including linear programming, integer programming, and nonlinear programming, and 3) gain familiarity with several commercial and open source optimization solvers and modeling languages.