COMP53215: Algorithms and Complexity

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• Provide knowledge and critical understanding of paradigms, and fundamental ideas behind algorithms.
• Provide knowledge and critical understanding of paradigms, fundamental ideas and methods relating to computational complexity.
• Discuss the design and analysis of efficient algorithms.

• Algorithmic methods: e.g. divide and conquer, dynamic programming.
• Mitigating hardness: e.g. approximation, branch and bound, exact methods.
• Computational complexity: e.g. classes between L and EXPTIME, time and space hierarchy theorems, FPT and the W-hierarchy.
• Randomised Algorithms: e.g. QuickSort, randomised data structures.
• Probabilistic Methods: e.g. Monte Carlo methods, tail inequalities

Subject-specific Knowledge:

• By the end of this module, students should be able to demonstrate:
• a systematic understanding of core techniques for constructing algorithms.
• an advanced understanding of core concepts from computational complexity theory.
• an ability to design novel algorithms to solve specific complex problems.

Subject-specific Skills:

• By the end of this module, students should be able to demonstrate:
• familiarity with state-of-the-art algorithms to solve complex challenges and understanding of computational complexity implications.
• competence to translate abstract problem descriptions into algorithmic formulations; competent and educated selection of appropriate solution algorithms.

Key Skills:

• By the end of this module, students should be able to demonstrate:
• familiarity with advanced paradigms and modern algorithms underlying computer science applications, and their analysis

• Lectures enable students to learn the core material relevant to the topic and problem classes enable students to develop their theoretical understanding and problem-solving skills.
• Summative assessments and formative exercises encourage students to focus their ability to independently analyse and solve problems as it relates to the topic of algorithms and complexity.

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