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.  

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

• Presentation and/or works
• Written examination

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

• Presentation and/or works
• Written examination

Q3 UE Assessment Comments

idem Q1

Type of Resit Assessment for UE in Q1 (BAB1)

• Presentation and/or works
• Written examination

Q1 UE Resit Assessment Comments (BAB1)

idem Q1