Module Details

The information contained in this module specification was correct at the time of publication but may be subject to change, either during the session because of unforeseen circumstances, or following review of the module at the end of the session. Queries about the module should be directed to the member of staff with responsibility for the module.
Title Advanced Algorithmic Techniques
Code COMP523
Coordinator Dr J Sylvester
Computer Science
John.Sylvester@liverpool.ac.uk
Year CATS Level Semester CATS Value
Session 2023-24 Level 7 FHEQ Second Semester 15

Aims

To provide a sound foundation concerning the design and analysis of advanaced discrete algorithms.
To provide a critical rational concerning advanced complexity theory and algorithmics.
To provide an in-depth, systematic and critical understanding of selected significant issues at the forefront of research explorations in the design and analysis of discrete algorithms.


Learning Outcomes

(LO1) Describe the following classes of algorithms and design principles associated with them: recursive algorithms, graph (search-based) algorithms, greedy algorithms, algorithms based on dynamic programming, network flow (optimisation) algorithms, approximation algorithms, randomised algorithms, distributed and parallel algorithms.

(LO2) Illustrate the above mentioned classes by examples from classical algorithmic areas, current research and applications.

(LO3) Identify which of the studied design principles are used in a given algorithm taking account of the similarities and differences between the principles.

(LO4) Apply the studied design principles to produce efficient algorithmic solutions to a given problem taking account of the strengths and weaknesses of the applicable principles.

(LO5) Outline methods of analysing correctness and asymptotic performance of the studied classes of algorithms, and apply them to analyse correctness and asymptotic performance of a given algorithm.

(S1) Critical thinking and problem solving - Critical analysis

(S2) Critical thinking and problem solving - Evaluation

(S3) Critical thinking and problem solving - Problem identification

(S4) Critical thinking and problem solving - Creative thinking

(S5) Numeracy/computational skills - Reason with numbers/mathematical concepts

(S6) Numeracy/computational skills - Problem solving

Syllabus
 

Recursive Algorithms (Divide & Conquer) ~ 3 Lectures

Graph Algorithms ~ 3 lectures

Greedy Algorithms ~ 3 Lectures

Dynamic Programming ~ 2 Lectures

Network flows ~ 3 Lectures

NP-Completeness ~ 3 Lectures

Linear Programming ~ 1 Lecture

Approximation Algorithms ~ 5 Lectures

Randomised Algorithms ~ 4 lectures

Online Algorithms ~ 2 Lectures

Teaching and Learning Strategies

Teaching Method 1 - Lecture
Description:
Teaching Method 2 - Tutorial
Description:

Standard on-campus delivery
Teaching Method 1 - Lecture
Description: Mix of on-campus/on-line synchronous/asynchronous sessions
Teaching Method 2 - Tutorial
Description: On-campus synchronous sessions

Teaching Schedule
  Lectures Seminars Tutorials Lab Practicals Fieldwork Placement Other TOTAL
Study Hours 30

   10

       40
Timetable (if known)              
Private Study 110
TOTAL HOURS 150

Assessment
EXAM Duration Timing
(Semester)		 % of
final
mark		 Resit/resubmission
opportunity		 Penalty for late
submission		 Notes
(523) Final exam  150    70       
CONTINUOUS Duration Timing
(Semester)		 % of
final
mark		 Resit/resubmission
opportunity		 Penalty for late
submission		 Notes
(523.1) Assessment 1 There is a resit opportunity. Standard UoL penalty applies for late submission. This is not an anonymous assessment. Assessment Schedule (When) :Semester 2  0    15       
(523.2) Assessment 2 There is a resit opportunity. Standard UoL penalty applies for late submission. This is not an anonymous assessment. Assessment Schedule (When) : Semester 2  0    15       

Recommended Texts
Reading lists are managed at readinglists.liverpool.ac.uk. Click here to access the reading lists for this module.