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Description
This module aims to give an introduction to optimisation and its role in operations research, which is the efficient allocation of resources toward some end. It covers formulation of deterministic and random models and methods for making decisions within them. It is primarily intended for third year undergraduates registered on the degree programmes offered by the Department of Statistical Science (including the MASS programmes). The academic prerequisite for these students听(in addition to their compulsory modules) is STAT0007.
Intended Learning Outcomes
- describe typical operations research problems and the role of optimisation in addressing them;
- design, analyse and solve optimisation problems;
- interpret results of optimisation, assess their underlying assumptions, and make improvements;
- describe, construct and implement optimisation algorithms.
Applications - Optimisation methods provide the means for successful business strategies, scientific planning and statistical estimation under constraints. They are a critical component of any area where decision making under limited resources is necessary.听The algorithms covered in this module are both mathematically interesting and applicable to a wide variety of complex real life situations.
Indicative Content - Convex functions. Convex optimisation problems. Algorithms for solution of convex optimisation problems. Sequential decision processes, including dynamic programming problems. Exact and approximate solution methods. Applications from a variety of areas in operations research and applied probability.
Key Texts - Available from .
Module deliveries for 2024/25 academic year
Last updated
This module description was last updated on 19th August 2024.
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