Math 510. Special Topics. Fall or Spring [1, 2, or 3 credits, depending upon material] Staff. A treatment of topics of interest not routinely covered by existing courses. Mathematical statistics and game theory are two recent topics offered that are of interest to COR graduate students.

Math 524. Operations Research II---Stochastic Models. Spring [3] Lawrence. Pre-requisite: Math 501 or equivalent probability background. A survey of probabilistic Operations Research models and applications. Topics include stochastic processes, Markov chains, queueing theory and applications, Markovian decision processes, inventory theory, and decision analysis.

Math 532. Combinatorial theory. Spring of even numbered years [3] Li. A discussion of combinatorial theory and applications to practical problems. The topics covered include: graph theory, graphical algorithms, elementary principles of enumeration, the inclusion-exclusion principle, Polya counting principle, recurrence relations, generating functions. Additional topics such as combinatorial designs, coding theory, Boolean algebra and Switching functions, may be included as time permits.

CSci 526. Simulation. Fall [3] Staff. Pre-requisites: Calculus, Data Structures. Introduction to simulation. Discrete and continuous stochastic models, random number generation, elementary statistics, simulation of queuing and inventory systems, Monte Carlo simulation, point and interval parameter estimation.

CSci 529. Computer Organization and Programming Languages. Spring [3]. Pre-requisite: CSci 539 or the equivalent. Topics include computer performance evaluation, computer organization at the machine language level, computer arithmetic, syntax and semantics of programming languages, variable binding, scopes, types, expressions, statement level control structures, parameter passing, and activation records. Runtime support of high-level programming languages at the machine language level will be a focus of the course.

CSci 539. Data Structures and Algorithms. Fall [4]. Pre-requisites: Knowledge of C or C++. Data structures and their representations, data abstraction, internal representation. Specific data structures, including lists, stacks, queues, tress, priority queues, hash tables, and their applications. Systematic study of algorithms, their complexity, and programming implementation. Survey of methods for achieving high algorithmic efficiency by using good data structures and sophisticated design. Advanced features of C++, including classes, inheritance, and polymorphism.

CSci 608. Statistical Decision Theory. Fall [3] Schaefer. 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 616. Stochastic Models in Computer Science. Fall [3] Staff. Pre-requisites: Knowledge of Discrete Mathematics and Calculus. An introduction to stochastic models, problem solving, and expected value analysis as applied to algorithms and systems in computer science. Topics include probability, discrete and continuous random variables, discrete-time Markov chains, and continuous-time birth-death processes.

CSci 618. Models and Applications in Operations Research. Fall [3] Schaefer. 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] Schaefer. 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 653. Analysis of Algorithms. Fall [3] Mao. Pre-requisite: CSci 503 or CSci 539. Algorithm design techniques including divide-and-conquer, dynamic programming and greedy method. Analysis methods including worst case and average case. Additional topics chosen from among amortized analysis, lower bound theory and NP-completeness.

CSci 658. Discrete Optimization. Spring [3] Kincaid. 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] Leemis. 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 670. Colloquium. Fall and Spring (1) Staff. Each full-time graduate student is required to enroll in this course. No credits earned in this course may be applied to the number of credits required for a degree.

CSci 678. Statistical Analysis of Simulation Models. Spring [3] Leemis. 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 680. Statistical Computing. Fall [3] Trosset. Pre-requisites: Probability, Statistics. Topics include linear regression, linear least squares, matrix factorization, nonlinear regression, Gauss-Newton methods, maximum likelihood estimation, parameter estimation, quasi-Newton methods, Monte Carlo integration, and bootstrap methods.

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.

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.

CSci 726. Discrete Event Simulation. Spring [3] Staff. Pre-requisite: CSci 526. Methods of discrete event simulation. Markov chains. Simulation of open and closed networks of queues. Simulation of non-stationary Poisson processes. Transient and steady-state analysis. Event list algorithms and data structures. Theoretical and empirical tests of randomness.

CSci 736. Discrete Linear Systems. Spring [3] Staff. Pre-requisite: Calculus, Linear Algebra, Data Structures, CSci 616. Modeling and analysis of discrete linear systems. The sampling theorem, Nyquist frequency and aliasing. Digital filters. Convolution, the discrete and fast Fourier transform. Data compression, coding, transmission and reconstruction. Information theory, signal-to-noise ratio and noise suppression. Selected applications

CSci 746. Discrete-State Stochastic Models. Fall [3] Ciardo. Pre-requisite: CSci 616. Logic, performance, and reliability analysis of discrete-state systems. Exploration of the state space. Queueing networks, fault trees, reliability block diagrams, task graphs, Petri nets and domain-oriented languages. Underlying stochastic processes, solutions and approximations.