Study programme 2020-2021 | Français | ||
Optimization | |||
Programme component of Bachelor's in Engineering à la Faculty of Engineering |
Students are asked to consult the ECTS course descriptions for each learning activity (AA) to know what special Covid-19 assessment methods are possibly planned for the end of Q3 |
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Code | Type | Head of UE | Department’s contact details | Teacher(s) |
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UI-B3-IRCIVI-301-M | Compulsory UE | GILLIS Nicolas | F151 - Mathématique et Recherche opérationnelle |
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Language of instruction | Language of assessment | HT(*) | HTPE(*) | HTPS(*) | HR(*) | HD(*) | Credits | Weighting | Term |
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| Français | 36 | 24 | 0 | 0 | 0 | 5 | 5.00 | 1st term |
AA Code | Teaching Activity (AA) | HT(*) | HTPE(*) | HTPS(*) | HR(*) | HD(*) | Term | Weighting |
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I-MARO-035 | Linear Optimization | 14 | 14 | 0 | 0 | 0 | Q1 | |
I-MARO-036 | Non-Linear Optimization | 22 | 10 | 0 | 0 | 0 | Q1 |
Programme component |
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Objectives of Programme's Learning Outcomes
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.
Content of UE
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 of Assessment for UE in Q1
Q1 UE Assessment Comments
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.
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 of Assessment for UE in Q3
Q3 UE Assessment Comments
idem Q1
Type of Resit Assessment for UE in Q1 (BAB1)
Q1 UE Resit Assessment Comments (BAB1)
idem Q1
Type of Teaching Activity/Activities
AA | Type of Teaching Activity/Activities |
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I-MARO-035 |
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I-MARO-036 |
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Mode of delivery
AA | Mode of delivery |
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I-MARO-035 |
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I-MARO-036 |
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Required Reading
AA | |
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I-MARO-035 | |
I-MARO-036 |
Required Learning Resources/Tools
AA | Required Learning Resources/Tools |
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I-MARO-035 | Linear algebra |
I-MARO-036 | Not applicable |
Recommended Reading
AA | |
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I-MARO-035 | |
I-MARO-036 |
Recommended Learning Resources/Tools
AA | Recommended Learning Resources/Tools |
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I-MARO-035 | Slides |
I-MARO-036 | Not applicable |
Other Recommended Reading
AA | Other Recommended Reading |
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I-MARO-035 | Not applicable |
I-MARO-036 | Not applicable |