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

CodeTypeHead of UE Department’s
contact details
Teacher(s)
UI-B3-IRCIVI-301-MCompulsory UEGILLIS NicolasF151 - Mathématique et Recherche opérationnelle
  • GILLIS Nicolas
  • TUYTTENS Daniel
  • VANDAELE Arnaud

Language
of instruction
Language
of assessment
HT(*) HTPE(*) HTPS(*) HR(*) HD(*) CreditsWeighting Term
  • Français
Français362400055.001st term

AA CodeTeaching Activity (AA) HT(*) HTPE(*) HTPS(*) HR(*) HD(*) Term Weighting
I-MARO-035Linear Optimization1414000Q1
I-MARO-036Non-Linear Optimization2210000Q1

Overall mark : the assessments of each AA result in an overall mark for the UE.
Programme component
Corequis

Objectives of Programme's Learning Outcomes

  • Understand the theoretical and methodological fundamentals in science and engineering to solve problems involving these disciplines
    • Identify, describe and explain basic scientific and mathematical principles
    • Identify, describe and explain the basic principles of engineering particularly in their specialising field
    • Understand laboratory techniques: testing, measuring, monitoring protocol, and security
    • Select and rigorously apply knowledge, tools and methods in sciences and engineering to solve problems involving these disciplines
  • Collaborate, work in a team
    • Interact effectively with other students to carry out collaborative projects.
    • Analyse personal performance within a group
    • Identify and appropriately implement the different ways of working in a group

Learning Outcomes of UE

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

Linear part: Modeling and solving linear optimization problems (continous and discrete).  

Non-linear part: Be able to model and solve a nonlinear continuous optimization problem. Attendance at theory/exercises classes of at least 80% is required. Attendance at practical sessions and seminars is mandatory.  

UE Content: description and pedagogical relevance

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

Linear part: model, resolution with the simplex method in the continous case and with branch and bound in the discrete case.   
Non-linear part: The objective of this course is to provide students with the basic tools to address and solve nonlinear optimization problems.The course will be divided into two main parts: modelization and methods
The first part aims to teach students to determine the type of optimization problems (linear, quadratic, convex, etc.) and to characterize optimal solutions in the more general context of problems with equality and inequality constraints.
In the second part, the most widespread numerical methods will be introduced.
Attendance at theory/exercises classes of at least 80% is required. Attendance at practical sessions and seminars is mandatory.  

Prior Experience

Mathematics (first and second year classes)

Type(s) and mode(s) of Q1 UE assessment

  • Written examination - Face-to-face
  • Production (written work, report, essay, collection, product, etc.) - To be submitted in class
  • Oral examination - Face-to-face

Q1 UE Assessment Comments

Students are evaluated based on small projects during the semester, and by a final written exam and an oral exam

Method of calculating the overall mark for the Q1 UE assessment

Gobal evaluation: 50% linear, 50% non-linear. You need at least 7/20 in each AA, otherwise you receive the lowest grade among the two. 
For students who have not met the 80% attendance rule, assessment will be based on a specific exam to be described by the professor before the exam.
In the case where the student has respected the constraints of attendance (see description of the course), the following rules apply:
The final grade (/20) of the AA is based on three grades:
- a grade A (/20) to evaluate the theoretical understanding of the course during a written exam
- a grade B (/20) of personal works / homeworks
- a grade C (/20) to evaluate the ability to implement algorithms and methods to solve nonlinear problems during an oral exam and/or practical works
The computation of the final grade of the AA is done as follows:
If the three grades A, B, and C are greater than or equal to 6 (that is, at least 6/20, or 30% in all three parts), then: finalgrade = (9*A + 5*B + 6*C) / 20.
If one of the three grades A, B or C is less than 6, then the final grade will be equal to the minimum grade, that is: finalgrade = minimum(A, B, C).
-> In the case where the student has not respected the constraints of attendance (see description of the course), the following rules apply:
The AA grade will be assessed during an oral exam.  

Type(s) and mode(s) of Q1 UE resit assessment (BAB1)

  • N/A - Néant

Q1 UE Resit Assessment Comments (BAB1)

n/a

Method of calculating the overall mark for the Q1 UE resit assessment

n/a

Type(s) and mode(s) of Q3 UE assessment

  • Written examination - Face-to-face
  • Production (written work, report, essay, collection, product, etc.) - To be submitted in class
  • Oral examination - Face-to-face

Q3 UE Assessment Comments

idem Q1

Method of calculating the overall mark for the Q3 UE assessment

idem Q1

Type of Teaching Activity/Activities

AAType of Teaching Activity/Activities
I-MARO-035
  • Cours magistraux
  • Conférences
  • Exercices dirigés
  • Utilisation de logiciels
  • Démonstrations
I-MARO-036
  • Cours magistraux
  • Conférences
  • Exercices dirigés
  • Utilisation de logiciels
  • Démonstrations

Mode of delivery

AAMode of delivery
I-MARO-035
  • Face-to-face
I-MARO-036
  • Face-to-face

Required Learning Resources/Tools

AARequired Learning Resources/Tools
I-MARO-035Linear algebra
I-MARO-036Not applicable

Recommended Learning Resources/Tools

AARecommended Learning Resources/Tools
I-MARO-035Slides
I-MARO-036Not applicable

Other Recommended Reading

AAOther Recommended Reading
I-MARO-035Not applicable
I-MARO-036Not applicable
(*) 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 : 19/06/2023
Date de dernière génération automatique de la page : 04/05/2024
20, place du Parc, B7000 Mons - Belgique
Tél: +32 (0)65 373111
Courriel: info.mons@umons.ac.be