| First cycle degree courses | Second cycle degree courses | Single cycle degree courses |
| School of Science | ||
| COMPUTER SCIENCE | ||
Course unit
METHODS AND MODELS FOR COMBINATORIAL OPTIMIZATION
SC01122975, A.A. 2016/17
Information concerning the students who enrolled in A.Y. 2015/16
METHODS AND MODELS FOR COMBINATORIAL OPTIMIZATION
SC01122975, A.A. 2016/17
Information concerning the students who enrolled in A.Y. 2015/16
Information on the course unit
| Degree course |
Second cycle degree in COMPUTER SCIENCE SC1176, Degree course structure A.Y. 2014/15, A.Y. 2016/17 |
 bring this page with you |
|---|---|---|
| Number of ECTS credits allocated | 6.0 | |
| Type of assessment | Mark | |
| Course unit English denomination | METHODS AND MODELS FOR COMBINATORIAL OPTIMIZATION | |
| Website of the academic structure | http://informatica.scienze.unipd.it/2016/laurea_magistrale | |
| Department of reference | Department of Mathematics | |
| Mandatory attendance | No | |
| Language of instruction | English | |
| Branch | PADOVA | |
| Single Course unit | The Course unit can be attended under the option Single Course unit attendance | |
| Optional Course unit | The Course unit can be chosen as Optional Course unit |
Lecturers
| Teacher in charge | LUIGI DE GIOVANNI | MAT/09 | |
|---|---|---|---|
| Other lecturers | MARCO DI SUMMA | MAT/09 |
Mutuated
| Course unit code | Course unit name | Teacher in charge | Degree course code |
|---|---|---|---|
| INP5070470 | METHODS AND MODELS FOR COMBINATORIAL OPTIMIZATION | LUIGI DE GIOVANNI | IN2191 |
ECTS: details
| Type | Scientific-Disciplinary Sector | Credits allocated | |
|---|---|---|---|
| Educational activities in elective or integrative disciplines | MAT/09 | Operational Research | 6.0 |
Course unit organization
| Period | First semester |
|---|---|
| Year | 2nd Year |
| Teaching method | frontal |
| Type of hours | Credits | Teaching hours |
Hours of Individual study |
Shifts |
|---|---|---|---|---|
| Practice | 0.5 | 4 | 8.5 | No turn |
| Laboratory | 1.5 | 12 | 25.5 | No turn |
| Lecture | 4.0 | 32 | 68.0 | No turn |
| Start of activities | 01/10/2016 |
|---|---|
| End of activities | 20/01/2017 |
| Show course schedule | 2019/20 Reg.2014 course timetable |
Examination board
| Board | From | To | Members of the board |
|---|---|---|---|
| 6 a.a. 2018/2019 | 01/10/2018 | 28/02/2020 |
DE GIOVANNI
LUIGI
(Presidente)
DI SUMMA MARCO (Membro Effettivo) CONFORTI MICHELANGELO (Supplente) DE FRANCESCO CARLA (Supplente) RINALDI FRANCESCO (Supplente) |
| 5 a.a. 2017/2018 | 01/10/2017 | 28/02/2019 |
DE GIOVANNI
LUIGI
(Presidente)
CONFORTI MICHELANGELO (Membro Effettivo) DE FRANCESCO CARLA (Membro Effettivo) DI SUMMA MARCO (Membro Effettivo) RINALDI FRANCESCO (Membro Effettivo) |
| Prerequisites: | Basic notions of Operations Research, Linear Programming, and computer programming. |
| Target skills and knowledge: | Basic use of advanced decision support methods for modelling and solving combinatorial optimization problems. Use of mathematical and algorithmic tools for solving optimization problems of practical relevance by software packages and optimization libraries. |
| Examination methods: | Oral examination about course contents. Each student may chose to present a short project concerning models and exact/heuristic solution methods for a realistic application of combinatorial optimization. |
| Assessment criteria: | The examination evaluates to what extent the student has learned the notions presented and her/his ability to apply them to the solution of practical combinatorial optimization problems. |
| Course unit contents: | 1. Advanced linear programming and duality with applications: primal-dual simplex, column generation, applications to network
optimization. 2. Advanced methods for Integer Linear Programming (ILP): Branch & Bound and relaxation techniques, alternative ILP formulations, cutting planes method and Branch & Cut, application to relevant examples (Traveling Salesman Problem, location, network design etc.). 3. Meta-heuristics for combinatorial optimization: local search based, evolutionary algorithms. 4. Application of graph modeling and optimization. 5. Labs: optimization software packages and libraries. |
| Planned learning activities and teaching methods: | Classes. Labs. Discussion about case studies. During labs, some basic optimization algorithms will be implemented, both exact (using integer programming libraries) and heuristic. |
| Additional notes about suggested reading: | Lecture notes provided by the teacher. Articles from scientific journals. |
| Textbooks (and optional supplementary readings) |
|