An introduction to important and commonly-used models in analytics, as well as aspects of the modeling process.

Topics include modeling with networks and graphs; linear, nonlinear, and integer programming, construction of models employing modern modeling languages; and general solution strategies.

Independent research conducted under the guidance of a faculty member.

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Introduce IE/OR/ACO PhD graduate students to faculty and research topics, discuss and demonstrate basic graduate student resources, and acclimatize students to graduate school.

This course studies the theoretical foundations of linear and integer programming with focus on algorithmic results. Topics covered will include algorithms for linear programming, Farkas lemma, duality, simplex method, polarity, lattices, basis reduction, algorithms for integer linear programming, total unimodularity, theory of cutting-planes.