Combinatorial Optimization
Objectives
(i) Comprehension of the main concepts from graphs, polyhedra; and matroids
(ii) Development of algorithm designing skills;
(iii) Improving modeling skills;
(iv) Improving mathematical maturity;
General characterization
Code
10809
Credits
6.0
Responsible teacher
Manuel Valdemar Cabral Vieira
Hours
Weekly - 4
Total - 56
Teaching language
Português
Prerequisites
Students are supposed to have knowledge in Linear Programming, and some skills in algorithms.
Bibliography
L.A. Wolsey, Integer Programming, Wiley, 1998.
D.B. West, Introduction to Graph Theory, Prentice Hall, 2001
A. Schrijver, Combinatorial Optimization: Polyhedra and Efficiency. Algorithms and Combinatorics, Springer, 2003
A. Schrijver, A Course in Combinatorial Optimization, 2013
B. Korte, J. Vygen, Combinatorial Optimization: Theory and Algorithms, Springer, 2008
Teaching method
In this course classes are theoretical-practical.
Lectures consist of expose of theoretical concepts and carry up some proffs, using the framework and the projector, and resolution of exercises and problems.
Part of the study is done independently by the student, individually and in groups, using bibliographic brackets, and the support of the teacher in the classroom, and in pre-established schedules reserved for the students'''''''''''''''' attendence.
Evaluation method
Rules of evaluation
The student may be evaluated by two tests and by a written work and will be approved if the two tests and the written work sum up at least 10.
The student may also be approved by a final exam if the exam''''s grade is at least 10. The grade will be the one attained in the exam (rounded) and any grade in any test will be discarded.
Any grade improvement may be done in the final exam and will be subject to the same demands as the exam.
An additional project and/or oral exam is required for grades above 17.
Subject matter
1. Graphs
2. Modeling with binary variables
3. Integer programming
4. Trees
5. Shortest paths
6. Matchings
7. Flows
8. Matroids
9. Computational complexity
10. Approximation algorithms