First cycle
degree courses
Second cycle
degree courses
Single cycle
degree courses
School of Science
MATHEMATICS
Course unit
DISCRETE OPTIMIZATION
SCL1001382, A.A. 2018/19

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

Information on the course unit
Degree course First cycle degree in
MATHEMATICS
SC1159, Degree course structure A.Y. 2008/09, A.Y. 2018/19
N0
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Number of ECTS credits allocated 6.0
Type of assessment Mark
Course unit English denomination DISCRETE OPTIMIZATION
Website of the academic structure http://matematica.scienze.unipd.it/2018/laurea
Department of reference Department of Mathematics
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 MARCO DI SUMMA MAT/09

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 Second semester
Year 2nd Year
Teaching method frontal

Type of hours Credits Teaching
hours
Hours of
Individual study
Shifts
Practice 2.0 16 34.0 No turn
Lecture 4.0 32 68.0 No turn

Calendar
Start of activities 25/02/2019
End of activities 14/06/2019

Syllabus
Prerequisites: Basic knowledge in Linear Algebra are required.
Target skills and knowledge: The student will learn the basic theory of Discrete Optimization, along with the main resolution techniques and the possible practical applications of this theory.
Examination methods: A written exam is mandatory, which allows the student to reach the score of 30/30. The written exam is designed to verify that the student has gained understanding of the theory and algorithms presented in this course.
An oral exam is optional, and can be exploited to increase the score without repeating the written exam.
Assessment criteria: The student has to prove his/her understanding of the theoretical results and the algorithms presented in the course, and his/her capability to solve exercises.
Course unit contents: Some fundemental topics in Discrete Opitmization will be illustrated:
- Linear Programming problems;
- Geometric aspects of Linear Programming;
- The simplex method;
- Duality in Linear Programming;
- Basics of graphs and algorithm complexity;
- The shortest path problem;
- The maximum flow and minimum cut problem.
Planned learning activities and teaching methods: Lectures, including exercises.
Additional notes about suggested reading: Notes written by the teacher.
Textbooks (and optional supplementary readings)

Innovative teaching methods: Teaching and learning strategies
  • Lecturing
  • Problem based learning
  • Loading of files and pages (web pages, Moodle, ...)

Innovative teaching methods: Software or applications used
  • Moodle (files, quizzes, workshops, ...)
  • Latex