menu
William and Mary
search

COR Courses

These are the core COR courses.

The following courses count towards both the 32 credit requirement of the MS degree and the required seven xx8 courses. Recommended electives that count toward the 32 credit requirement only can be found here.

CSci 608. Statistical Decision Theory. Fall [3]. Pre-requisite: Equivalent of Math 351. Development and use of systematic procedures for assisting decision makers in evaluating alternative choices. Emphasis is on problem formulation, uncertainty and risk assessment, Bayes, minimax and other decision rules and applications. Problems will be solved using appropriate software tools.

CSci 618. Models and Applications in Operations Research. Fall [3]. Pre-requisite: Equivalent of Math 323. A study of realistic and diverse Operations Research problems with emphasis upon model formulation, interpretation of results, and implementation of solutions. Topics include applications of linear programming, goal programming, decomposition of large-scale problems, and job scheduling algorithms. Problems will be solved using appropriate software packages.

CSci 628. Linear Programming. Fall [3]. Pre-requisite: Equivalent of Math 211. Co-requisite: Equivalent of Math 241. Theory and applications of linear programming. Topics include the simplex method, duality theory, sensitivity analysis, and interior point methods. Problems will be solved using appropriate software tools.

CSci 638. Nonlinear Programming [3]. Pre-requisite: CSci 628 and the equivalent of Math 212. Topics include unconstrained optimization, nonlinear least squares, feasible-point methods, and penalty and barrier methods, with an emphasis on effective computational techniques.

CSci 648. Network Optimization. Spring [3]. Pre-requisite: CSci 628. Network flow theory and algorithms, including transportation, maximum flow, shortest path, and minimum spanning tree problems. Applications to a variety of areas are also stressed. Problems will be solved using appropriate software tools.

CSci 658. Discrete Optimization. Spring [3]. Pre-requisite: CSci 628 and the equivalent of CSci 303. Topics include relaxation techniques, constructive heuristics, improving search techniques (simplex method, simulated annealing, and tabu search), branch and bound schemes, and valid inequalities for branch and cut methods. Problems will be solved using appropriate software tools.

CSci 668. Reliability. Spring [3]. Pre-requisites: Equivalent of Math 401 and CSci 141. Introduction to probabilistic models and statistical methods used in analysis of reliability problems. Topics include models for the lifetime of a system of components and statistical analysis of survival times data. Problems will be solved using appropriate software tools.

CSci 678. Statistical Analysis of Simulation Models. Spring [3]. Pre-requisites: Equivalent of Math 351, Math 401 and CSci 141. This courses introduces statistical techniques used in the analysis of simulation models. The first half of the course develops techniques for determining appropriate inputs to a simulation model, and the last half develops analysis techniques that are applied to the output of a simulation model.

CSci 688. Topics. Fall or Spring (1, 2, or 3 credits, depending on material) Staff. Pre-requisite: Will be published in he preregistration schedule. May be repeated for different topics. A treatment of Master's level topics of interest not routinely covered by existing courses. Material may be chosen from various areas of computational operations research. Topics classes that have been offered in the past include convex optimization, statistical computing, regression, design of experiments, facility location theory, computational probability, bootstrapping, scale-free networks, stochastic optimization, and knowledge discovery.

CSci 698. Introduction to Simulation. Fall [3] Leemis. Pre-requisites: Equivalent of Math 401 and CSci 241. Simulation model building in a high-level simulation language (SIMAN) with C++/C interface. Topics include network, discrete-event, and continous modeling apporaches. Interfaces between the three modeling approaches are presented. Familiarity with univariate and multivariate probability distributions is required for input modeling and simulation output analysis. Course culminates in a semester project in SIMAN.

CSci 708. Research Project in Computational Research. Fall and Spring (2, 2) Staff. Graded P (Pass) or F (Failure). Pre-requisite: Permission of Graduate Director. Students will select a faculty advisor and committee in their area of specialization within computational operations research, prepare a research proposal abstract for approval by the department's director of graduate studies, undertake a research project, and write a paper describing their research. This course is normally taken after a student has completed 18 credit hours toward the M.S. degree with a specialization in computational operations research. Not open to students who receive credit for either CSci 700 or CSci 710.