CS5929 Discrete Optimisation

Academic year

2024 to 2025 Flexible study

Further information on which modules are specific to your programme.

Key module information

SCOTCAT credits

15

The Scottish Credit Accumulation and Transfer (SCOTCAT) system allows credits gained in Scotland to be transferred between institutions. The number of credits associated with a module gives an indication of the amount of learning effort required by the learner. European Credit Transfer System (ECTS) credits are half the value of SCOTCAT credits.

SCQF level

SCQF level 11

The Scottish Credit and Qualifications Framework (SCQF) provides an indication of the complexity of award qualifications and associated learning and operates on an ascending numeric scale from Levels 1-12 with SCQF Level 10 equating to a Scottish undergraduate Honours degree.

Availability restrictions

Available only to students studying the PG Cert/PG Dip/MSc in Data Science (Digital)

Module coordinator

Dr B Varghese

This information is given as indicative. Staff involved in a module may change at short notice depending on availability and circumstances.

Module Staff

TBC: Module coordinator(s): Computer Science (cs5929.staff@st-andrews.ac.uk)

This information is given as indicative. Staff involved in a module may change at short notice depending on availability and circumstances.

Module description

Many problems in modern Data Science, such as planning and scheduling, involve solutions with integer values and options for solutions that grow in size very rapidly. This module covers the theory, tools and technologies developed and used to solve problems in Integer Programming and Combinatorial Optimization. Data science problems often require a solution from a discrete set of values. Examples include scheduling, resource allocation, and path selection. For many important problems, a numeric optimization formulation can be useful for finding optimal or near-optimal solutions. In other cases, the generality provided by discrete optimization tools and techniques may be more efficient in the identification of high-quality solutions and may also supply useful additional information that can be leveraged to solve future problems. For CS5929 the types of optimisation include greedy methods; local search (Hill Climbing, Simulated Annealing); linear programming; constraint programming; and hybrid methods (LNS).

Assessment pattern

Coursework = 100%

Re-assessment

Coursework = 100%

Learning and teaching methods and delivery

Weekly contact

Students should expect to engage in approximately six tutorials over the course of the module, which will be scheduled with an awareness of the pace at which they are progressing, rather than at a fixed time each week. Students should consider the amount of independent study time this module involves when planning their learning.

Scheduled learning hours

6

The number of compulsory student:staff contact hours over the period of the module.

Guided independent study hours

148

The number of hours that students are expected to invest in independent study over the period of the module.

Intended learning outcomes

  • Understand core concepts in the field of Discrete Optimisation.
  • Be able to use declarative modelling for optimisation.
  • Be able to use candidate approaches for solving these problems, including Constraint Programming, Boolean Satisfiability (SAT), SAT Modulo Theories (SMT).
  • Understand how discrete optimisation can be applied together with machine learning.
  • Understand how a particular alternative (model, solver, and configuration) can be robustly chosen for a given problem.

CS5929 Discrete Optimisation (15 credits)

Academic year

2024 to 2025 Full Year

Further information on which modules are specific to your programme.

Key module information

SCOTCAT credits

15

The Scottish Credit Accumulation and Transfer (SCOTCAT) system allows credits gained in Scotland to be transferred between institutions. The number of credits associated with a module gives an indication of the amount of learning effort required by the learner. European Credit Transfer System (ECTS) credits are half the value of SCOTCAT credits.

SCQF level

SCQF level 11

The Scottish Credit and Qualifications Framework (SCQF) provides an indication of the complexity of award qualifications and associated learning and operates on an ascending numeric scale from Levels 1-12 with SCQF Level 10 equating to a Scottish undergraduate Honours degree.

Availability restrictions

Available only to students studying the PG Cert/PG Dip/MSc in Data Science (Digital)

Module coordinator

Dr B Varghese

This information is given as indicative. Staff involved in a module may change at short notice depending on availability and circumstances.

Module Staff

TBC: Module coordinator(s): Computer Science (cs5929.staff@st-andrews.ac.uk)

This information is given as indicative. Staff involved in a module may change at short notice depending on availability and circumstances.

Module description

Many problems in modern Data Science, such as planning and scheduling, involve solutions with integer values and options for solutions that grow in size very rapidly. This module covers the theory, tools and technologies developed and used to solve problems in Integer Programming and Combinatorial Optimization. Data science problems often require a solution from a discrete set of values. Examples include scheduling, resource allocation, and path selection. For many important problems, a numeric optimization formulation can be useful for finding optimal or near-optimal solutions. In other cases, the generality provided by discrete optimization tools and techniques may be more efficient in the identification of high-quality solutions and may also supply useful additional information that can be leveraged to solve future problems. For CS5929 the types of optimisation include greedy methods; local search (Hill Climbing, Simulated Annealing); linear programming; constraint programming; and hybrid methods (LNS).

Assessment pattern

Coursework = 100%

Re-assessment

Coursework = 100%

Learning and teaching methods and delivery

Weekly contact

Students should expect to engage in approximately six tutorials over the course of the module, which will be scheduled with an awareness of the pace at which they are progressing, rather than at a fixed time each week. Students should consider the amount of independent study time this module involves when planning their learning.

Scheduled learning hours

6

The number of compulsory student:staff contact hours over the period of the module.

Guided independent study hours

148

The number of hours that students are expected to invest in independent study over the period of the module.

Intended learning outcomes

  • Understand core concepts in the field of Discrete Optimisation.
  • Be able to use declarative modelling for optimisation.
  • Be able to use candidate approaches for solving these problems, including Constraint Programming, Boolean Satisfiability (SAT), SAT Modulo Theories (SMT).
  • Understand how discrete optimisation can be applied together with machine learning.
  • Understand how a particular alternative (model, solver, and configuration) can be robustly chosen for a given problem.