Enrolment options

MS-E2145 - Combinatorial Optimization D, Lecture, 7.1.2025-1.4.2025

Please note! Course description is confirmed for two academic years, which means that in general, e.g. Learning outcomes, assessment methods and key content stays unchanged. However, via course syllabus, it is possible to specify or change the course execution in each realization of the course, such as how the contact sessions are organized, assessment methods weighted or materials used.

LEARNING OUTCOMES

Upon completing this course, the student can:
- Use the theory and the algorithms covered in the course to solve real-life complex problems involving combinatorial and integer optimization.
- Familiarise themselves with classical problems involving graphs. 
- Understand the difference between approximation, heuristics and greedy algorithms and know where to apply them as appropriate. 
- Benefit from basic knowledge of complexity theory and its application.
- Assess whether a given combinatorial optimization problem can be solved efficiently (in polynomial time).
- Master the main techniques for modelling and solving typical combinatorial problems in practice. 
- Use optimization software to implement and solve combinatorial optimization problems.

Credits: 5

Schedule: 07.01.2025 - 01.04.2025

Teacher in charge (valid for whole curriculum period):

Teacher in charge (applies in this implementation): Cunha Dias

Contact information for the course (applies in this implementation):

CEFR level (valid for whole curriculum period):

Language of instruction and studies (applies in this implementation):

Teaching language: English. Languages of study attainment: English

CONTENT, ASSESSMENT AND WORKLOAD

Content

  • valid for whole curriculum period:

    In this course, the student will learn the fundamentals of combinatorial optimization theory and practice and also how these can be applied to real-world optimization problems. Topics addressed in the course include but are not limited to networking optimization, complexity theory, polyhedral theory, exact methods and approximation algorithms.

    After the course, the student can analyse the main characteristics of an optimization problem, build models of such problems using standard techniques and choose appropriate solution techniques.  

Assessment Methods and Criteria

  • valid for whole curriculum period:

    - projects and homework assignments

Workload

  • valid for whole curriculum period:

    The course will be taught by a composition of the following methods:
    - lectures;
    - theoretical and computational exercises;
    - project assignment and feedback.

DETAILS

Study Material

  • valid for whole curriculum period:

    Lecture notes and lecture slides

    Textbook: Korte, B., and Vygen, J. (2011). Combinatorial optimization, volume 1. Springer.

Substitutes for Courses
Prerequisites

FURTHER INFORMATION

Further Information

  • valid for whole curriculum period:

    Teaching Language: English

    Teaching Period: 2024-2025 Spring III - IV
    2025-2026 Spring III - IV

Rules of Conduct - Students´ Rights and Responsibilities (in force from 1 August 2020)

Aalto University Code of Academic Integrity and Handling Violations Thereof

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