Study programme 2023-2024Français
Optimisation
Programme component of Bachelor's in Computer Science (MONS) (day schedule) à la Faculty of Science

CodeTypeHead of UE Department’s
contact details
Teacher(s)
US-B2-SCINFO-008-MCompulsory UEGILLIS NicolasF151 - Mathématique et Recherche opérationnelle
  • GILLIS Nicolas
  • TUYTTENS Daniel

Language
of instruction
Language
of assessment
HT(*) HTPE(*) HTPS(*) HR(*) HD(*) CreditsWeighting Term
  • Français
Français141400044.001st term

AA CodeTeaching Activity (AA) HT(*) HTPE(*) HTPS(*) HR(*) HD(*) Term Weighting
I-MARO-035Linear Optimization1414000Q1100.00%

Programme component
Corequis
Corequis

Objectives of Programme's Learning Outcomes

  • Understand the fundamentals of computer science
    • Show an understanding and deep knowledge of the concepts of computer science and mathematical formalisms used in the field of computer science
    • Solve exercises and computer problems by applying basic knowledge in the various disciplines of computer science
    • Use the vocabulary and the correct mathematical reasoning to formulate and solve problems in the field of computer science
    • Use and combine knowledge from different disciplines to solve multidisciplinary problems
  • Manage IT projects
    • Manage a project in compliance with specifications, constraints and deadlines
    • Creatively implement knowledge and expertise gained in the field of computer science.
    • Apply appropriate technological and scientific ICT approaches
    • Demonstrate independence and their ability to work in teams.
  • Understand the fundamentals related to scientific methods
    • Develop skills of abstraction and modelling through a conceptual and scientific approach
    • Conduct rigorous reasoning based on scientific arguments
    • Give a critique and argue a point of view regarding multiple-source information and other opinions

Learning Outcomes of UE

- Model an optimization problem- Choose a suitable method to solve an optimization problem- Develop and apply optimization methods

UE Content: description and pedagogical relevance

This class analyzes models and methods for linear optimization problems (continuous and discrete). 

Prior Experience

Mathematics (first and second year classes)

Type of Teaching Activity/Activities

AAType of Teaching Activity/Activities
I-MARO-035
  • Cours magistraux
  • Conférences
  • Exercices dirigés
  • Utilisation de logiciels
  • Démonstrations

Mode of delivery

AAMode of delivery
I-MARO-035
  • Face-to-face

Required Learning Resources/Tools

AARequired Learning Resources/Tools
I-MARO-035Linear algebra

Recommended Learning Resources/Tools

AARecommended Learning Resources/Tools
I-MARO-035Slides

Other Recommended Reading

AAOther Recommended Reading
I-MARO-035Not applicable

Grade Deferrals of AAs from one year to the next

AAGrade Deferrals of AAs from one year to the next
I-MARO-035Unauthorized

Term 1 Assessment - type

AAType(s) and mode(s) of Q1 assessment
I-MARO-035
  • Written examination - Face-to-face

Term 1 Assessment - comments

AATerm 1 Assessment - comments
I-MARO-0351 written Examen, 100% within the AA, duration: 2h.          

Resit Assessment - Term 1 (B1BA1) - type

AAType(s) and mode(s) of Q1 resit assessment (BAB1)
I-MARO-035
  • Written examination - Face-to-face

Term 3 Assessment - type

AAType(s) and mode(s) of Q3 assessment
I-MARO-035
  • Written examination - Face-to-face

Term 3 Assessment - comments

AATerm 3 Assessment - comments
I-MARO-0351 written Examen, 100% within the AA, duration: 2h.          
(*) HT : Hours of theory - HTPE : Hours of in-class exercices - HTPS : hours of practical work - HD : HMiscellaneous time - HR : Hours of remedial classes. - Per. (Period), Y=Year, Q1=1st term et Q2=2nd term
Date de dernière mise à jour de la fiche ECTS par l'enseignant : 21/03/2023
Date de dernière génération automatique de la page : 27/04/2024
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Tél: +32 (0)65 373111
Courriel: info.mons@umons.ac.be