OR/IT Doctoral Courses

796, 797 Directed Reading and Research (1-3:0:0).

Reading and research on a specific topic in information technology under the direction of a faculty member. May be repeated as needed.

819 Computational Models for Probabilistic Inference (3:3:0).
Prerequisite: SYST 664.

Graphical models for encoding conditional independence assumptions in a multivariate discrete probability distribution. Includes computational methods for updating probabilities when evidence is observed on some variables in the model. Algorithms for finding the most probable instantiation of the network. Applications in expert systems and decision analysis.

842 Models of Probabilistic Reasoning (3:3:0).
Prerequisite: STAT 544 and OR 681.

Survey of alternative views about how incomplete, inconclusive, and possibly unreliable evidence might be evaluated and combined. Among the views discussed are the Bayesian, Baconian, Shafer-Dempster, and Fuzzy systems for probabilistic reasoning.

879 Topics in Stochastic System Simulation (3:3:0).
Prerequisite: OR 635 or permission of instructor.

Special topics and recent developments in the Monte Carlo simulation methodology for discrete-event stochastic systems. Contents vary and possible topics include statistical analysis of simulation output data, random number and random ariate generation, variance reduction techniques, sensitivity analysis and optimization of simulation models, distributed and parallel simulation, object-oriented simulation, and specialized applications. May be repeated for credit when topics are distinctly different.

880 Queuing Modeling of Computer-Communication Networks (3:3:0).
Prerequisite: OR 645, 647; ECE 542; or equivalents.

Study of analytical modeling of computer and communication networks and performance evaluations. Topics include Markovian systems, open networks, closed networks, approximations, decomposition, simulation, sensitivity analysis, and optimal operation of systems. Local area networks, manufacturing systems, and other applications are presented.

882 Advanced Topics in Combinatorial Optimizations (3:3:0).
Prerequisites: OR 641 and 642.

Study of problems using the most recent developments. Topics include cutting plane procedures based on polyhedral combinatorics, column-generation procedures for large complex problems, heuristic approaches (genetic algorithms, simulated annealing, tabu search), the study of special structures, reformulation techniques and bounding approaches. Topics stress the most recent developments in the field. May be repeated for credit when topics are distinctly different.

884 Advanced Topics in Nonlinear Programming (3:3:0).
Prerequisite: OR 644.

Study of theory and algorithms for solving nonlinear optimization problems. Contents vary, and possible topics include large-scale and parallel-unconstrained optimization, theoretical issues in constrained optimization, duality theory, Lagrangian and sequential quadratic programming methods. May be repeated for credit when topics are distinctly different.