Numerical Linear Algebra
1.0 Title: Numerical Linear Algebra
Date: September 1, 2008
Credit Hours: 4
Prerequisite(s): 1016-331-Linear Algebra I (1016-432- one ntial-equations/solving-linearly-independent.html">Linear Algebra
recommended)
Corequisite(s): None
Course proposed by: School of Mathematical Sciences
2.0 Course information:
Contact hours | Maximum students/section | |
Classroom | 4 | 20 |
Lab | ||
Studio | ||
Other (specify _______) |
Quarter(s) offered (check)
___ Fall __X__ Winter ____ Spring _____ Summer
Students required to take this course: (by program
and year, as appropriate)
Applied and Computational Mathematics graduate students in the Scientific
Computing concentration.
Students who might elect to take the course:
Graduate students and advanced undergraduate students in mathematics,
physics, imaging science, and engineering.
3.0 Goals of the course (including rationale for
the course , when appropriate):
To be able to apply matrix formulations in problem solving, to learn canonical
decompositions used in developing matrix-based algorithms and to be able to use
existing software packages in solving matrix -based problems
4.0 Course description (as it will appear in the
RIT Cata log , including pre - and corequisites,
quarters offered)
1016–712 Numerical Linear Algebra
This course is a rigorous study of theoretical concepts and computational
issues in
linear algebra. Topics include an analysis of Gaussian elimination with
pivoting , its
error and its stability, iterative methods for solving linear systems, matrix
factorizations , eigenvalues, singular value decomposition, Krylov subspace
methods
and application to least squares, systems of nonlinear equations and partial
differential equations . This course requires independent study of certain topics
that
are not covered in the class lectures. Software packages like MATLAB will be
utilized through several computing projects. (1016-331, 1016-432 recommended)
Class 4, Credit 4 (W)
5.0 Possible resources (texts, references, computer
packages, etc.)
5.1 Kincaid, D., Cheney, W., F., Numerical Analysis, Brooks/Cole
5.2 Stewart, G., Afternotes on Numerical Analysis, SIAM
5.3 Trefethen, L., Bau, D., Numerical Linear Algebra, SIAM
5.4 Tyrtyshnikov, E., A Brief Introduction to Numerical Analysis, Birkhauser
6.0 Topics (outline):
6.1 Direct methods for solving systems of linear equations
6.1.1. Gaussian elimination and back substitution
6.1.2. Pivoting
6.1.3. LU and Choleski decomposition
6.2 Error analysis
6.2.1. Vector and matrix norms
6.2.2. Rounding errors, forward and backward stability
6.2.3. Conditioning, perturbation analysis and residual
6.3 Iterative methods
6.3.1. Gauss-Jacobi and Gauss-Seidel
6.3.2. SOR
6.4 Eigenvalues
6.4.1. Power method , inverse method and shifts
6.4.2. Rayleigh quotient iteration
6.4.3. Orthogonal matrices and QR decomposition
6.4.4. QR algorithm
6.4.5. Schur decomposition
6.4.6. Hessenberg form
6.4.7. Singular value decomposition
6.4.8. Krylov subspace methods
6.5 Applications
6.5.1. Least squares, normal equations and pseudo-inverses
6.5.2. Newton and quasi-Newton methods for systems of nonlinear equations
6.5.3. Partial differential equations
7.0 Intended learning outcomes and associated
assessment methods of those
outcomes
Learning Outcomes | Assessment Methods | |||
Homework | Tests | Computer Projects |
Final Exam | |
7.1 Understand direct methods for solving systems of linear equations |
X | X | X | |
7.2 Study error analysis | X | X | X | X |
7.3 Compute iterative methods | X | X | X | X |
7.4 Compute eigenvalues | X | X | X | X |
7.5 Understand orthogonal matrices and QR decomposition |
X | X | X | |
7.6 Compute least squares, normal equations and pseudoinverses |
X | X | X | X |
7.7 Understand partial differential equations |
X | X | X |
8.0 Program or general education goals supported by
this course
8.1 To develop students’ understanding of the mathematical framework that
supports
engineering, science, and mathematics.
8.2 To develop a capacity for critical and analytical thinking.
8.3 To develop an appropriate level of mathematical literacy and competency.
9.0 Other relevant information (such as special
classroom, studio, or lab needs,
special scheduling, media requirements, etc.)
9.1 Computer laboratory facilities with appropriate software.
10.0 Supplemental information
None
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