Total Credits: 20
Level: Level 4
Target Students: MSc and undergraduate students (part II and part III) in the School of Computer Science and the School of Mathematics. Also available to students from other Schools with the agreement of the module convenor. Also available to Part II CS UG students subject to Part I performance.This module is part of the AI, Modelling and Optimisation theme in the School of Computer Science. Available to JYA/Erasmus students.
|Autumn||Assessed by end of Autumn Semester|
Prerequisites: Knowledge of algorithm basics, data structures and some computer programming. Knowledge of the basics of linear algebra and calculus is desirable.
Summary of Content: The module provides an entry point to computational optimization techniques, in particular for modelling and solving linear and discrete optimization problems. Computational optimization is one of the most important areas within operations research (OR), which is a discipline that uses modelling techniques, analytics and computational methods to solve complex problems in industry and business. In this module you will learn to interpret and develop algebraic models for a variety of real-world linear and discrete optimization problems to then use powerful optimization software (linear, integer and mixed-integer solvers) to produce a solution. The module covers topics such as linear programming, integer programming, combinatorial optimization, modelling and optimization software, and multi-objective optimization among others. Optimization technology is ubiquitous in today's world, for applications in logistics, finance, manufacturing, workforce planning, product selection, healthcare, and any other area where the limited resources must be used efficiently. You will spend around four hours per week in lectures and workshops for this module.
Method and Frequency of Class:
|Activity||Number Of Weeks||Number of sessions||Duration of a session|
|Lecture||11 weeks||1 per week||1 hour|
|Workshop||11 weeks||1 per week||2 hours|
|Computing||11 weeks||1 per week||1 hour|
Method of Assessment:
|Exam 1||50||1.5hr written examination (optimization problem modelling and solving)|
|Coursework 1||25||Apply modelling and optimization solvers to solve a real-world scenario and write a report about the assignment|
|Inclass Exam 1 (Written)||25||Weekly online test based on the workshops, include writing models and using optimization solvers|
Dr D Landa Silva
Education Aims: To develop an understanding of linear and discrete optimization, one of the most important areas within operations research. To develop the skills for modelling a range of optimization problems in business and industry using mathematical models. To implement those mathematical models using algebraic and spreadsheet optimization solvers in order to obtain solutions to the corresponding optimization problem. To develop skills for identifying, analyzing, modelling and solving real-world linear and discrete optimization problems.
Learning Outcomes: Knowledge and Understanding: Linear and discrete optimization from the computer science and mathematical perspectives. Algebraic models for linear and discrete optimization problems. Post-optimality analysis. Simplex method and Branch and Bound algorithm.Intellectual Skills: Analytical methods. Ubiquitous nature of optimization. Professional Practical Skills: Use of algebraic and spreadsheet optimization software. Solution of real-world optimization problems in a range of applications. Transferable Skills: Problem solving. Communication skills. Computer-based solutions and mathematical skills.
Offering School: Computer Science
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