Use the Tab and Up, Down arrow keys to select menu items.
Techniques for optimising smooth functions both with and without constraints present.
Course Information:Mathematical optimization is an important subject that has applications to many areas in science, data science, commerce and engineering. This course introduces some of the fundamental concepts, including describing what an optimization problem is, understanding what is meant by a solution, and introducing several of the foundational algorithms that can be used to tackle optimization problems. Emphasis is placed on algorithms and understanding both how and why they work.Topics Covered:This course focuses on the minimization of smooth functions of several variables, in both the unconstrained and constrained cases. Topics may be chosen from the following list: Gradient Descent; Newton’s method; quasi-Newton methods; Conjugate Gradients; Augmented Lagrangian methods; Interior point methods; Trust region methods; Convexity; Positive Definiteness; Derivative approximations.
At the end of the course, students will:· Be familiar with the foundational algorithms for optimization;· Understand why, when and how these algorithms work;· Have observed computational aspects of the algorithms via software;· Have had the opportunity to learn problem solving skills both as part of a team and as an individual;· Have developed written and oral communications skills, emphasizing the ability to explain what the mathematics means.
Subject to approval of the Head of School.
EMTH604
Students must attend one activity from each section.
Rachael Tappenden
Domestic fee $1,206.00
International Postgraduate fees
* All fees are inclusive of NZ GST or any equivalent overseas tax, and do not include any programme level discount or additional course-related expenses.
For further information see Mathematics and Statistics .