M ziane A DER

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

1.2. Algorithms and Complexity

      Exercises Session 1.2

1.3. The Class P

      Exercises Session 1.3

1.4. The Class NP

      Exercises Session 1.4

1.5. The Class NP-Complete

 

Chapter 2 : Approximation

 

2.1. The PO and NPO classes

      Exercises Session 2.1

2.2. The class APX

      Exercises Session 2.2

2.3. The classes PTAS and FPTAS

      Exercises Session 2.3

2.4. The classes Log-APX and Poly-APX

      Exercises Session 2.4

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. Basic techniques for modeling a discrete optimization problem by a linear or quadratic convex program 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 (Master 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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