MTH 654 and MTH 659 (Numerical Analysis)
Large scale scientific computing methods - Fall 2009
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General information
INSTRUCTOR: Malgorzata Peszynska
CLASS: MWF 9:00-9:50, Gilkey 115, CRN: 14956 (MTH 654) or 14958 (MTH 659)
COURSE INFORMATION: In this class we develop theory and implementation details for solving large scale scientific computing problems. Rigorous mathematical background as well as algorithms will be developed for solving large linear and nonlinear systems of equations using a variety of Newton-Krylov methods, multigrid and domain decomposition.
Students will be introduced to parallel computing and will learn how to function in a high performance computing environment. A module on emerging architectures such as multicore, and on programming GPUs for the needs of scientific computing using NVIDIA CUDA programming environment, will be included, with a focus on applications point of view.
Additional topics such as a primer on numerical optimization, heuristic and probability-based methods will be included as time permits and/or given as individual projects.
There will be a lab meeting once a week (Fridays in MLC Kidd 108J) with mandatory attendance.
STUDENTS:
The course is intended for graduate students of mathematics and other disciplines and for well-prepared undergraduates. No specific preparation beyond solid undergraduate background in mathematics will be assumed. Knowledge of numerical methods, and familiarity with computer programming are a plus but are not required: students will be graded based on their learning derivative. Students are encouraged to contact me with questions about the class.

SEQUENCE MTH 654-656 in 2009-2010:
This course is the first in a year-long sequence, and the courses in this sequence can be taken independently. In the Winter and Spring the courses MTH 655, 656 will be taught by different instructors.
GRADING:
  1. Attendance at all labs Fridays in MLC Kidd 108J is required. Please contact me if you have to miss a lab meeting.
  2. You have to complete all lab projects and turn in a required lab summary. Quality of the work and the derivative of your learning curve rather than the absolute measure of performance will determine your grade. Go to assignments to see what is required.
  3. For those registered for full credit, (short) papers and/or presentations on individually assigned projects will be required. The projects will be assigned based on your background and interests.
Special arrangements for students with disabilities, make-up exams etc.: please contact the instructor and Services for Students with Disabilities, if relevant, and provide appropriate documentation.
Course drop/add information is at http://oregonstate.edu/registrar/.