Operational Research Complements
A.Y. 2018/2019
Learning objectives
The course aims at presenting some algorithmic techniques in Operations Research, for solving mixed-integer linear programming as well as non-linear programming problems.
Expected learning outcomes
Knowledge about algorithms to solve NP-hard optimization problems.
Lesson period: First semester
Assessment methods: Esame
Assessment result: voto verbalizzato in trentesimi
Single course
This course cannot be attended as a single course. Please check our list of single courses to find the ones available for enrolment.
Course syllabus and organization
Linea Crema
Responsible
Lesson period
First semester
Course syllabus
1. Integer and mixed-integer linear programming.
· Linear programming (recall).
· Geometric interpretation of (M)ILP, polyhedral theory.
· Cutting planes.
2. Implicit enumeration algorithms
· Branch-and-bound, branch-and-cut.
· Balas' algorithm for 0-1 ILP
· Dynamic programming
3. Lagrangean relaxation
4. Column generation, branch-and-price
5. Non-linear programming
· Unconstrained optimization
· Constrained optimization
· Linear programming (recall).
· Geometric interpretation of (M)ILP, polyhedral theory.
· Cutting planes.
2. Implicit enumeration algorithms
· Branch-and-bound, branch-and-cut.
· Balas' algorithm for 0-1 ILP
· Dynamic programming
3. Lagrangean relaxation
4. Column generation, branch-and-price
5. Non-linear programming
· Unconstrained optimization
· Constrained optimization
Website
Professor(s)