Study programme 20202021  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 Covid19 assessment methods are possibly planned for the end of Q3 

Code  Type  Head of UE  Department’s contact details  Teacher(s) 

USM1INFO60052M  Optional UE  VANDAELE Arnaud  F151  Mathématique et Recherche opérationnelle 

Language of instruction  Language of assessment  HT(*)  HTPE(*)  HTPS(*)  HR(*)  HD(*)  Credits  Weighting  Term 

 Français  22  10  0  0  0  3  3.00  1st term 
AA Code  Teaching Activity (AA)  HT(*)  HTPE(*)  HTPS(*)  HR(*)  HD(*)  Term  Weighting 

IMARO036  NonLinear Optimization  22  10  0  0  0  Q1  100.00% 
Programme component 

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 

IMARO036 

Mode of delivery
AA  Mode of delivery 

IMARO036 

Required Reading
AA  

IMARO036 
Required Learning Resources/Tools
AA  Required Learning Resources/Tools 

IMARO036  Not applicable 
Recommended Reading
AA  

IMARO036 
Recommended Learning Resources/Tools
AA  Recommended Learning Resources/Tools 

IMARO036  Not applicable 
Other Recommended Reading
AA  Other Recommended Reading 

IMARO036  Not applicable 
Grade Deferrals of AAs from one year to the next
AA  Grade Deferrals of AAs from one year to the next 

IMARO036  Unauthorized 