Numerical Linear Algebra

Course aim

The aim is also to deliver basic concepts in the theory of Numerical Linear Algebra, which is essential for the efficient solution of Linear Systems and, in particular, for taking profit advantage of current and increasingly parallel capabilities of computers for solving large-scale optimization problems.

Course content

Vector norms, Matrix norms, orthogonality and the Singular Value Decomposition, Sensitivity of Square Linear Systems, Conditioning of Linear Systems, Direct Methods for Linear Systems: Back substitution, Gauss elimination, LU and Cholesky factorisation. Orthogonalization and Least Squares: QR Factorization. Iterative (Indirect) Methods for Linear Systems: Jacobi, Gauss-Seidel, Conjugate Gradients, Eigenvalues: Definition and Basic properties, Hessenberg form, Power Methods, QR algorithm.

Textbook(s)

Golub, G.H. and Van Loan, C.F 1996, Matrix Computations, 3rd edition, Johns Hopkins University Press

Prerequisite(s)

Common to all relevant programs
Subject Area & Catalogue Number	Course Name
MATH 1056	Linear Algebra

Corequisite(s)

Nil

Teaching method

Component	Duration
INTERNAL, MAWSON LAKES
Lecture	3 hours x 13 weeks
Directed Study	N/A x 13 weeks
Workshop	2 hours x 13 weeks
Seminar	1 hour x N/A

Note: These components may or may not be scheduled in every study period. Please refer to the timetable for further details.

Assessment

Assignment 1, Assignment 2, Examination

Fees

EFTSL*: 0.125
Commonwealth Supported program (Band 2)
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Fee-paying program for domestic and international students
International students and students undertaking this course as part of a postgraduate fee paying program must refer to the relevant program home page to determine the cost for undertaking this course.

Non-award enrolment
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* Equivalent Full Time Study Load. Please note: all EFTSL values are published and calculated at ten decimal places. Values are displayed to three decimal places for ease of interpretation.