Module COMP52915: Advanced Algorithms: Coping with Intractability

Department: Computer Science

COMP52915: Advanced Algorithms: Coping with Intractability

Prerequisites

• None

Corequisites

• None

Excluded Combination of Modules

• None

Aims

• To give students a deeper knowledge of algorithmic solutions for typical computer science problems.
• To extend the students’ knowledge of the latest advances in understanding the limits of computation and the ways of coping with computational hardness.
• To give the students some experience with applying the theoretical knowledge obtained under the first two aims in a more practical setting.

Content

• Computability (existence of computational tasks without algorithms for their computation
• Computational hardness (in particular, NP-completeness)
• Theoretical methods of comping with computation hardness:
• 1) Tractable classes of intractable problems.
• 2) Approximation algorithms
• 3) Parameterized algorithms
• 4) Exact exponential algorithms.
• Applied methods of coping with NP-hardness:
• 1) SAT solving
• 2) Local search

Learning Outcomes

• By the end of this module, students should be able to demonstrate:
• a comprehensive and also practical understanding of how the theory of computation is applied in the design of algorithms.
• a critical evaluation of different approaches to the algorithmic solution for computationally hard problems.
• a critical awareness of some of the latest advances in research on various aspects of computation.

• By the end of this module, students should be able to demonstrate:
• an ability to critically apply notions from the theory of computation.
• an ability to choose and evaluate the best way to tackle computationally hard problems.
• an ability to judge research on the cutting edge of the theory of computation.

• By the end of this module, students should be able to demonstrate:
• an ability to abstract and solve problems both in a theoretical and practical way.
• an ability to learn independently.
• an ability to communicate technical information.

Modes of Teaching, Learning and Assessment and how these contribute to the learning outcomes of the module

• Lectures enable the students to learn new material relevant to the theory of computation.
• Formative homework exercises and practical classes identify areas where further independent research could be conducted and learnt knowledge can be applied to specific algorithmic challenges.
• The summative assessment tests the knowledge acquired and the students' ability to use this knowledge to solve complex problems.

Teaching Methods and Learning Hours

Summative Assessment

Formative Assessment:

Via computer classes.

■ Attendance at all activities marked with this symbol will be monitored. Students who fail to attend these activities, or to complete the summative or formative assessment specified above, will be subject to the procedures defined in the University's General Regulation V, and may be required to leave the University