Practical optimisation
4 points * First semester * Clayton * Prerequisites: MAT2010, MAA2011 and MAA2032. MAT2020 recommended
Numerical techniques and applications in unconstrained and constrained optimisation. Unconstrained optimisation: conditions for local minima, ad hoc methods, Newton-like methods, conjugate direction methods, sums of squares and nonlinear equations. Constrained optimisation: linear programming, simplex method, degeneracy, Lagrange multipliers, first and second order conditions, convexity, duality; quadratic programming, general linearly constrained optimisation, nonlinear programming; other optimisation problems, non-smooth optimisation.
Assessment
Examinations (1.5 hours): 85% * Assignments: 15%
Recommended texts
Cameron N Introduction to linear and convex programming Australian Mathematical Society Lecture Series No.1, CUP, 1985
Fletcher R Practical methods of optimization 2nd edn, Wiley, 1987