Overview
MATH3161 is a Mathematics Level III course.
Units of credit: 6
Prerequisites: 12 units of credit in Level 2 Mathematics courses including MATH2011 or MATH2111, and MATH2501 or MATH2601, or both MATH2019(DN) and MATH2089, or both MATH2069(CR) and MATH2099.
Exclusion: MATH5165
Cycle of offering: Term 1
Graduate attributes: The course will enhance your research, inquiry and analytical thinking abilities.
More information: The course outline contains information about course objectives, assessment, course materials and the syllabus.
Important additional information as of 2023
UNSW Plagiarism Policy
The University requires all students to be aware of its policy on plagiarism.
For courses convened by the School of Mathematics and Statistics no assistance using generative AI software is allowed unless specifically referred to in the individual assessment tasks.
If its use is detected in the no assistance case, it will be regarded as serious academic misconduct and subject to the standard penalties, which may include 00FL, suspension and exclusion.
The Online Handbook entry contains up-to-date timetabling information.
If you are currently enrolled in MATH3161, you can log into UNSW Moodle for this course.
Course aims
The concept of optimization (finding the "best" way to do something) arises across all branches of mathematics. It applies in areas ranging from biology and engineering to business and finance. This course provides an introduction to the theory of multi-variable optimization and optimal control. It aims to instil students with the skills to formulate, solve and analyze solutions to certain multi-variable optimisation problems and infinite dimensional optimal control problems.
Course description
Optimisation problems occur when you seek the values of variables to maximize or minimize an objective function subject to constraints on which variables are allowed. They are common throughout the physical and biological sciences, along with economics, finance and engineering.
This course looks at the formulation of optimisation problems as mathematical problems, characterizing solutions using necessary and/or sufficient optimality conditions. It also looks at modern numerical methods and software for solving the problems. Both finite dimensional problems (which involve a vector of variables) and infinite dimensional problems (where the variables are functions) are covered. This includes linear and nonlinear programming and optimal control problems.