Courses

Courses

SIE 500C - Introduction to SIE Methods: Linear Programming (1 unit)
Linear programming models, solution techniques, and duality.

SIE 540 - Survey of Optimization Method (3)
Survey of methods including network flows, integer programming, nonlinear programming, and dynamic programming. Model development and solution algorithms are covered. Graduate-level requirements include additional assigned readings and a project paper.

SIE 542 - Game Theory (3)
I II An introduction to the theory and applications of non-cooperative and cooperative games including conflict resolution. return to top

SIE 544 - Linear Programming (3)
Linear programming formulations, simplex method, geometry of the simplex
method, sensitivity and duality, and interior point methods.

SIE 545 - Foundations of Optimization (3)
II Unconstrained and constrained optimization problems from a numerical standpoint. Topics include variable metric methods, optimality conditions, quadratic programming, penalty and barrier function methods, interior point methods, successive quadratic programming methods.

SIE 546 - Algorithms, Graphs, & Networks (3)
II Model formulation and solution of problems on graphs and networks. Topics include heuristics and optimization algorithms on shortest paths, min-cost flow, matching and traveling salesman problems.

SIE 547 - Computational Issues in Optimization (3)
Efficient implementation techniques, complexity theory, data structures, and stabiltiy issues arising in the context of optimization algorithms. Students will examine classical pedagogical case studies as well as contemporary issues in operations research algorithmic design.

SIE 548 - Operations Research Modeling
In this course we consider applications that use and extend material from SIE 500. The key features of the course will be a set of case studies and associated technical materials that will enable the students to obtain proficiency in the following areas: making decisions as to the appropriate modeling tool to use based on the problem setting, the constraints on the solution process, the needs of the decision-maker, and the data available, building models and obtaining solutions that consider the typical issues that arise when solving real problems - multiple objectives, multiple constraints, stochastic and deterministic elements, sensitivity analysis and unknown data, working in teams in a distance setting.

SIE 640 - Large-Scale Optimization (3)
Decomposition-coordination algorithms for large-scale mathematical programming. Methods include generalized Benders decomposition, resource and price directive methods, subgradient optimization, and descent methods of nondifferentiable optimization. Application of these methods to stochastic programming will be emphasized.

SIE 644 - Integer and Combinatorial Optimization (3)
Modeling and solving problems where the decisions form a discrete set. Topics include model development, branch and bound methods, cutting plane methods, relaxations, computational complexity, and solving well-structured problems.

SIE 645 - Nonlinear Programming (3)
Numerical Methods of Nonlinear Programming. This course focuses on numerical issues for both unconstrained and constrained optimization problems. Starting with computational issues associated with Newton and Quasi-Newton methods, the course progresses to linearly constrained problems, and then nonlinearly constrained problems. It is expected that by the end of the semester, students will have implemented their very own version of sequential quadratic programming.

SIE 649 - Topics in Optimization
Research areas of current interest will be the focus of this class.

MIS 696D - Models for Quantative Analysis
Develops quantitative approaches for analyzing systems and managerial problems. Both deterministic and stochastic models including decision analysis game theory, linear and non-linear optimization, combinatorial optimization, and dynamic programming will be covered. back to top