MCS 504 - Mathematics and Information Science for Industry Workshop.
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Instructor: R. Grossman
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Text: None; selected articles will be used.
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Offered: Spring Semester, 1999
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Offered: 00765 LECD 0200-0500 F 0700 SEO
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Course Description: This course is centered around
one or more "industrial" problems. The goal of the course is
to provide an opportunity for students to use mathematics and
information sciences to work on problems arising from industrial
applications. The course will cover: mathematical modeling, problem
formulation, problem analysis, problem solution, developing software
to implement the solution, validating the software, analyzing the
results, documenting the problem and its solution, techniques for
effectively working in groups, software engineering, and effectively
communicating technical material.
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Comments: The course may be repeated for credit.
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Prerequisites: Prior course work in data structures and algorithms and C/C++ programming
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MCS 571 - Numerical Methods for Partial Differential Equations
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Instructor: F. Hanson
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Text: K.W. Morton and D.F. Mayers , Numerical Solution of Partial Differential Equations, Cambridge University Press.
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Offered: Spring Semester, 1999
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Timetable: 00780 LECD 0100-0150 M W F 0302 AH
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Course Description: (Bulletin) Finite difference methods for parabolic, elliptic and hyperbolic differential equations: explicit, Crank-Nicolson implicit, alternating directions implicit, Jacobi, Gauss-Seidel, successive over-relaxation, conjugate gradient, Lax-Wendroff, Fourier stability.
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Prerequisites: MCS 471(Numerical Analysis) or consent of the instructor.
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The MCS 571 web page has further information for this course.
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MCS 575 - Computer Performance Evaluation
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Instructor: C. Tier
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Text: P. J. B. King, Computer and Communications Performance Evaluation, Prentice Hall.
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Offered: Spring Semester, 1999
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Timetable: 00799 LECD 0100-0150 M W F 0311 AH
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Course Description: (Bulletin) Modeling of computer systems, basic queues, central server models, Little s Law, operational analysis, Markovian networks, Jackson and BCMP networks, product form solutions, computational algorithms, mean value analysis, approximation methods.
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Comments: The web page for this course is
MCS 575
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Prerequisites: Background in probability and stochastic processes
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