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The unit of study provides an introduction to engineering optimisation, focusing specifically on practical methods for formulating and solving linear, nonlinear and mixed-integer optimisation problems that arise in science and engineering. The unit covers conventional optimisation techniques, including unconstrained and constrained single- and multivariable optimisation, convex optimisation, linear and nonlinear programming, mixed-integer programming, and sequential decision making using dynamic programming. The emphasis is on building optimisation models, understanding their structure and using off-the-shelf solvers to solve them. While the unit is designed with engineers in mind, it provides sufficiently rigorous mathematical treatment to allow deeper study. The application focus is on the optimisation problems arising in electrical engineering, including power systems, communications, signal processing, control and computer engineering. The unit will use Matlab and AMPL as modelling tools and a range of state-of-the-art solvers, including Cplex, Gurobi, Knitro and Ipopt.
Code | ELEC5213 |
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Academic unit | Electrical and Information Engineering |
Credit points | 6 |
Prerequisites:
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None |
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Corequisites:
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None |
Prohibitions:
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None |
Assumed knowledge:
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Linear algebra, differential calculus, and numerical methods. Competency at programming in a high-level language (such as Matlab or Python) |
At the completion of this unit, you should be able to:
Unit outlines will be available 2 weeks before the first day of teaching for the relevant session.
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