Méziane AÏDER

Current teachings

Master 1 Romarin: ``Combinatorial Optimization’’

Courses (slides)

Assignments

1.      Introduction to combinatorial optimization

•      Exercise session 1

2.      Relaxations and bounds

•      Exercise session 2

3.      Branch and bound methods

•      Exercise session 3

4.      Cutting plane methods

•      Exercise session 4

5.      Polyhedral approaches

•      Exercise session 5

6.      Decomposition methods

•      Exercise session 6

7.      Reduction methods

•      Exercise session 7

                                                                      

Master 1 Romarin, BigdataA: ``Computational Complexity’’

Courses (slides)

Assignments

Chapter 1 : Complexity

 

1.1. Problems

•      Exercises session 1

1.2. Algorithms and complexity

•      Exercises session 2

1.3. The class P

•      Exercises session 3

1.4. The class NP

•      Exercises session 4

1.5. The class NP-complete

 

Chapter 2 : Approximation

 

2.1. The PO and NPO classes

 

2.2. The class APX

 

2.3. The classes PTAS and FPTAS

 

2.4. The classes Log-APX and Poly-APX

 

2.5. The Structure of NPO

 

 

Master 2 Romarin: ``Numerical Discrete Optimization’’

1.     General Introduction  

2.     Linear programming and convex quadratic programming

3.     Complement to mixed linear programming.

4.    Modeling discrete optimization problems by linear or quadratic convex programs in mixed variables.

5.     Solving continuous nonlinear problems with mixed linear programming.

6.      Complement to combinatorial optimization.

 

Doctoral Formations (all specialties): Didactics

Courses (slides)

1.      Chapter 1: Introduction to Didactics

2.      Chapter 2: Evaluation

3.      Chapter 3: Obstacles and errors

4.      Chapter 4: Problem situations

 

Doctoral Formation of Applied Mathematics, Specialities: ROM and OStoch

Courses (slides)

1.      Polyhedral approaches

2.      Matroid theory

3.      Intersection of matroids

4.      Advanced complexity theory

5.      Graph colorings

 

Past teachings

1.     Post-Graduation and Doctoral School

•    Advanced combinatorial optimization.      

•    Optimization in large systems.

•    Stochastic programming.

•    Advanced graph theory.

2.     Graduation (Engineer and Master's degrees)

•     Graph theory (mMaster 1).

•     Linear programming (Master 1).

•     Combinatorial optimization (Master 1).

•     Computational complexity (Master 1).

•     Modeling and case studies (Master 1).

•     Optimization and case studies (Master 2).

•     New techniques of operations research (Master 2).

•     Numerical discrete optimization (Master 2).

•     Discrete optimization (Master 2).

3.     Under-graduation (Bachelor's and Licenses degrees)

•      Algebra (License 1).

•      Analysis I (License 1).

•      Analysis II (License 2).

•      Numerical Analysis (License 2).

 

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