![]() | Study programme 2020-2021 | Français | |
![]() | Optimisation non linéaire | ||
Programme component of Master's in Computer Science à la Faculty of Science |
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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US-M1-INFO60-052-M | Optional UE | VANDAELE Arnaud | 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 | 22 | 10 | 0 | 0 | 0 | 3 | 3.00 | 1st term |
AA Code | Teaching Activity (AA) | HT(*) | HTPE(*) | HTPS(*) | HR(*) | HD(*) | Term | Weighting |
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I-MARO-036 | Non-Linear Optimization | 22 | 10 | 0 | 0 | 0 | Q1 | 100.00% |
Programme component |
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Objectives of Programme's Learning Outcomes
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. Attendance 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
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 attendance (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
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 attendance (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)
Q1 UE Resit Assessment Comments (BAB1)
not applicable
Type of Teaching Activity/Activities
AA | Type of Teaching Activity/Activities |
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I-MARO-036 |
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Mode of delivery
AA | Mode of delivery |
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I-MARO-036 |
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Required Reading
AA | |
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I-MARO-036 |
Required Learning Resources/Tools
AA | Required Learning Resources/Tools |
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I-MARO-036 | Not applicable |
Recommended Reading
AA | |
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I-MARO-036 |
Recommended Learning Resources/Tools
AA | Recommended Learning Resources/Tools |
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I-MARO-036 | Not applicable |
Other Recommended Reading
AA | Other Recommended Reading |
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I-MARO-036 | Not applicable |
Grade Deferrals of AAs from one year to the next
AA | Grade Deferrals of AAs from one year to the next |
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I-MARO-036 | Unauthorized |