MATH412-19S2 (C) Semester Two 2019


15 points
15 Jul 2019 - 10 Nov 2019


Techniques for optimising smooth functions both with and without constraints present.

This course looks at unconstrained and constrained local minimisation of functions of several variables. The focus is largely on gradient based methods including steepest descent, Newton, quasi-Newton and conjugate gradient methods.

Constrained methods include augmented Lagrangian and sequential quadratic programming techniques. Direct search methods in global optimisation will also be looked at. These have the advantage of not relying on the availability or even existence of derivatives, at the cost of a reduction in speed.


Subject to approval of the Head of School.


Timetable 2019

Students must attend one activity from each section.

Lecture A
Activity Day Time Location Weeks
01 Tuesday 14:00 - 15:00 A9 Lecture Theatre 15 Jul - 25 Aug
9 Sep - 20 Oct
Lecture B
Activity Day Time Location Weeks
01 Wednesday 12:00 - 13:00 Jack Erskine 315 15 Jul - 25 Aug
9 Sep - 20 Oct
Lecture C
Activity Day Time Location Weeks
01 Monday 15:00 - 16:00 Jack Erskine 441 15 Jul - 25 Aug
9 Sep - 20 Oct

Course Coordinator

Chris Price (MATH)

Indicative Fees

Domestic fee $969.00

* Fees include New Zealand GST and do not include any programme level discount or additional course related expenses.

For further information see Mathematics and Statistics.

All MATH412 Occurrences

  • MATH412-19S2 (C) Semester Two 2019