First cycle
degree courses
Second cycle
degree courses
Single cycle
degree courses
School of Engineering
COMPUTER ENGINEERING
Course unit
MODELS AND SOFTWARE FOR DISCRETE OPTIMIZATION
INN1030564, A.A. 2019/20

Information concerning the students who enrolled in A.Y. 2017/18

Information on the course unit
Degree course First cycle degree in
COMPUTER ENGINEERING
IN0508, Degree course structure A.Y. 2011/12, A.Y. 2019/20
N0
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Degree course track Common track
Number of ECTS credits allocated 6.0
Type of assessment Mark
Course unit English denomination MODELS AND SOFTWARE FOR DISCRETE OPTIMIZATION
Department of reference Department of Information Engineering
E-Learning website https://elearning.dei.unipd.it/course/view.php?idnumber=2019-IN0508-000ZZ-2017-INN1030564-N0
Mandatory attendance No
Language of instruction Italian
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 DOMENICO SALVAGNIN MAT/09

ECTS: details
Type Scientific-Disciplinary Sector Credits allocated
Basic courses MAT/09 Operational Research 6.0

Course unit organization
Period First semester
Year 3rd Year
Teaching method frontal

Type of hours Credits Teaching
hours
Hours of
Individual study
Shifts
Lecture 6.0 48 102.0 No turn

Calendar
Start of activities 30/09/2019
End of activities 18/01/2020
Show course schedule 2019/20 Reg.2011 course timetable

Examination board
Board From To Members of the board
8 A.A. 2019/2020 01/10/2019 15/03/2021 SALVAGNIN DOMENICO (Presidente)
FISCHETTI MATTEO (Membro Effettivo)
7 A.A. 2018/2019 01/10/2018 15/03/2020 SALVAGNIN DOMENICO (Presidente)
FISCHETTI MATTEO (Membro Effettivo)

Syllabus
Prerequisites: - basic knowledge of linear algebra
Target skills and knowledge: - basic knowledge of the main optimization paradigms
- how to model optimization problems as mixed-integer linear programs (MIP) and/or as constraint programs (CP)
- how to uso algebraic modeling languages and solvers for MIP/CP
Examination methods: - written exam (open questions and modeling exercises)
- (optional) modeling project
Assessment criteria: - proficiency in modeling real world optimization problems
- knowledge of the basic concepts of the main optimization paradigms
Course unit contents: - mathematical programming/optimization
- linear and mixed-integer programming
- constraint programming
- algebraic modeling languages
Planned learning activities and teaching methods: - traditional lectures
- lab exercises with MIP/CP solvers and modeling tools
Additional notes about suggested reading: - lecture notes (available online)
Textbooks (and optional supplementary readings)

Innovative teaching methods: Teaching and learning strategies
  • Lecturing
  • Laboratory
  • Problem based learning
  • Working in group

Innovative teaching methods: Software or applications used
  • Latex
  • Python

Sustainable Development Goals (SDGs)
Quality Education Industry, Innovation and Infrastructure