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 large 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.

Students will be expected to give several class presentation on topics selected from the course material. In addition to multiple ungraded homework assignments, intended to introduce the student to the use of the MatLab Optimization toolbox, the AMPL modeling language, and IBM’s CPLEX Optimization Studio, the students will be expected to work on a semester-long optimization formulation & implementation, and to write a journal style research paper describing their results.

Final grades will be based on class participation and the final project.

No purchased textbook will be required. Several open access books, web resources, articles and research papers will compose the course readings.

Examples: