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This subject provides a rigorous introduction numerical nonlinear optimization, as used across all of science and particularly in engineering design. There is an emphasis on both the theory and application of optimization techniques, with a focus on solving unconstrained and constrained nonlinear programmes. This subject is intended for research higher-degree students in engineering.
Topics may include:
- Algorithms for unconstrained optimization
- Algorithms for constrained optimization
- Convex sets and functions
- Convex optimization problems
- Duality theory
- Computational complexity
- Approximation algorithms and penalty methods.
Intended learning outcomes
INTENDED LEARNING OUTCOMES (ILOs)
Having completed this subject it is expected that the student be able to:
- Demonstrate an in-depth understanding of numerical linear algebra and real analysis within the context of optimization problems
- Formulate and solve engineering problems via nonlinear optimisation methods
- Apply computational tools to solve standard unconstrained and constrained optimization problems.
On completion of this subject, students will have developed the following skills:
- Ability to apply knowledge of basic science and engineering fundamentals;
- In-depth technical competence in at least one engineering discipline;
- Ability to undertake problem identification, formulation and solution;
- Ability to utilise a systems approach to design and operational performance;
- Expectation of the need to undertake lifelong learning, capacity to do so;
- Capacity for independent critical thought, rational inquiry and self-directed learning;
- Profound respect for truth and intellectual integrity, and for the ethics of scholarship.
Last updated: 3 November 2022