## MA410E - Statistics

### Objectives

#### General objective:

Upon completion of this course the student will be able to:

- identify an optimization problem in a real-world context,

- formulate some simple continuous or combinatorial optimization problems in an appropriate modeling framework

- describe and apply manually a set of basic mathematical and graph optimization algorithms

- choose and apply an appropriate algorithm to solve basic optimization problems

#### Detailed objectives:

Upon completion of this course the student will be able to:

- identify and differentiate continuous and combinatorial optimization problems as well as linear and non-linear optimization problems,

- describe and model basic problems of graph theory : minimum weight spanning tree problem, shortest path problem, project scheduling problem, maximum flow problem,

- build linear/ non-linear mathematical optimization models

- describe and apply manually the following concepts and algorithms:

- optimality conditions,

- gradient methods,

- projected gradient methods,

- penalty methods,

- Simplex algorithm,

- branch and bound search,

- basic algorithms of graph theory : Prim, Kruskal, Dijkstra, Bellman and Ford-Fulkerson

#### Hours

- Lectures : 30

### In brief

ECTS credits 3

Number of hours 30

Teaching language

Anglais