| CODE | CCE2503 | ||||||||||||
| TITLE | Search and Optimisation Methods | ||||||||||||
| UM LEVEL | 02 - Years 2, 3 in Modular Undergraduate Course | ||||||||||||
| MQF LEVEL | 5 | ||||||||||||
| ECTS CREDITS | 5 | ||||||||||||
| DEPARTMENT | Communications and Computer Engineering | ||||||||||||
| DESCRIPTION | Search and Optimisation techniques are often at the core of many tasks or problems in Computer Science and Engineering, including scheduling, routing and machine learning applied in the areas of computer systems and networks, telecommunications, biomedical, transport, etc. This unit brings together the most commonly applied search and optimisation techniques and adopts both a theoretical and practical approach. The body of knowledge is delivered through lectures and assessed via one assignment and one end of term exam. At the end of the study-unit, the student will be able to solve a problem by casting it as an optimisation problem. Study-unit Aims: The unit aims to: - Teach how problems are cast an optimization problem and solve using one or more techniques; - Assist students with studying search and optimization techniques, i.e geometric, algebraic and algorithmic methods, gradient ascent and descent methods, and meta-heursitic methods; genetic algorithms, tabu search, simulated annealing; - Assist students with studying various classes and types of optimization problems, i.e. local and global, convex and concave, and single and multi-objective problems. Learning Outcomes: 1. Knowledge & Understanding By the end of the study-unit the student will be able to: - Differentiate between discrete and continuous optimisation problems; - Differentiate between local and global optimisation problem; - Differentiate between unconstrained and constrained problems; - DIfferentiate between convex and concave functions; - Differentiate between linear and nonlinear problems; - Differentiate between single and multi objective problems; - Differentiate between static, dynamic and stochastic problems; - Differentiate between integer, continuous and mixed-integer problems; - Describe typical examples of optimisation problems; - Describe the geometric solution to linear problems; - Formulate an algebraic solution to linear problems; - Describe the simplex method for linear problems; - Explain what a cost function is; - Describe the gradient method for non-linear and continuous problems; - Describe meta-heuristic or simulation based methods (genetic algorithms, tabu search, simulated annealing) for non-linear and discontinuous problems; - Compare the performance of meta-heuristics in local and global single objective optimisation problems. 2. Skills By the end of the study-unit the student will be able to: - Explain a problem in terms of a search space or cost function; - Find a solution to a problem using one or more optimisation methods; - Implement the solution in Python or equivalent. Main Text/s and any supplementary readings: Main Texts: - A First Course in Mathematical Modeling, Frank R. Giordano, William P. Fox, Steven B. Horton and Maurice D. Weir, 5th edition, Brooks/Cole, 2013. (library – QA401 .G55 2009 4th ed.) - Search and Optimization by Metaheuristics; Techniques and Algorithms Inspired by Nature, Ke-Lin Du and M.N.S Swamy, 1st edition, Springer, 2016. |
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| STUDY-UNIT TYPE | Lecture, Independent Study & Tutorial | ||||||||||||
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| LECTURER/S | Johann A. Briffa Gianluca Valentino |
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The University makes every effort to ensure that the published Courses Plans, Programmes of Study and Study-Unit information are complete and up-to-date at the time of publication. The University reserves the right to make changes in case errors are detected after publication.
The availability of optional units may be subject to timetabling constraints. Units not attracting a sufficient number of registrations may be withdrawn without notice. It should be noted that all the information in the description above applies to study-units available during the academic year 2024/5. It may be subject to change in subsequent years. |
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