Study programme 2021-2022Français
Nonlinear optimization
Programme component of Master's in Computer Science à la Faculty of Science

CodeTypeHead of UE Department’s
contact details
Teacher(s)
US-M1-SCINFO-077-MOptional UEVANDAELE ArnaudF151 - Mathématique et Recherche opérationnelle
  • VANDAELE Arnaud

Language
of instruction
Language
of assessment
HT(*) HTPE(*) HTPS(*) HR(*) HD(*) CreditsWeighting Term
  • Français
Français221000033.001st term

AA CodeTeaching Activity (AA) HT(*) HTPE(*) HTPS(*) HR(*) HD(*) Term Weighting
I-MARO-036Non-Linear Optimization2210000Q1100.00%
Programme component

Objectives of Programme's Learning Outcomes

  • Manage large-scale software development projects.
    • Apply, mobilise, articulate and promote the knowledge and skills acquired in order to help lead and complete a project.
  • Manage research, development and innovation.
    • Understand unprecedented problems in computer science and its applications.
    • Organise and lead a research, development or innovation project to completion.
  • Master communication techniques.
    • Communicate, both orally and in writing, their findings, original proposals, knowledge and underlying principles, in a clear, structured and justified manner.
  • Develop and integrate a high degree of autonomy.
    • Aquire new knowledge independently.
    • Pursue further training and develop new skills independently.

Learning Outcomes of UE

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.
The evaluation methods are likely to be adjusted according to the context imposed by the health measures.
The 80% rule of physical attendance would be transformed into a rule of 80% of online participation. Participation at practical sessions and seminars is still mandatory.

Content of UE

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. Participation at practical sessions and seminars is still mandatory.

The evaluation methods are likely to be adjusted according to the context imposed by the health measures.
The 80% rule of physical attendance would be transformed into a rule of 80% of online participation. Participation at practical sessions and seminars is still mandatory.

Prior Experience

Basic Mathematics (Analysis and Algebra), Numerical Analysis, basic programmation skills

Type of Assessment for UE in Q1

  • Presentation and/or works
  • Oral examination
  • Written examination
  • Practical test
  • Graded tests

Q1 UE Assessment Comments

-> 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 7, then: finalgrade = (9*A + 5*B + 6*C) / 20.
If one of the three grades A, B or C is less than or equal than 7, 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 participation (see description of the course), the following rules apply:
The AA grade will be assessed during an oral exam.

The evaluation methods are likely to be adjusted according to the context imposed by the health measures.
In all cases, the system described above (different parts and calculation of the mark) would be retained but the different grades would be obtained by remote evaluations.

Type of Assessment for UE in Q3

  • Presentation and/or works
  • Oral examination
  • Written examination
  • Practical Test
  • Graded tests

Q3 UE Assessment Comments

-> 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 7, then: finalgrade = (9*A + 5*B + 6*C) / 20.
If one of the three grades A, B or C is less than or equal than 7, 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 participation (see description of the course), the following rules apply:
The AA grade will be assessed during an oral exam.

The evaluation methods are likely to be adjusted according to the context imposed by the health measures.
In all cases, the system described above (different parts and calculation of the mark) would be retained but the different grades would be obtained by remote evaluations.

Type of Resit Assessment for UE in Q1 (BAB1)

  • N/A

Q1 UE Resit Assessment Comments (BAB1)

not applicable

Type of Teaching Activity/Activities

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

Mode of delivery

AAMode of delivery
I-MARO-036
  • Mixed

Required Reading

AA
I-MARO-036

Required Learning Resources/Tools

AARequired Learning Resources/Tools
I-MARO-036Not applicable

Recommended Reading

AA
I-MARO-036

Recommended Learning Resources/Tools

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

Other Recommended Reading

AAOther Recommended Reading
I-MARO-036Not applicable

Grade Deferrals of AAs from one year to the next

AAGrade Deferrals of AAs from one year to the next
I-MARO-036Unauthorized
(*) 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 génération : 17/09/2021
20, place du Parc, B7000 Mons - Belgique
Tél: +32 (0)65 373111
Courriel: info.mons@umons.ac.be