Checking date: 30/05/2022

Course: 2022/2023

Heuristics and Optimization
Study: Bachelor in Computer Science and Engineering (218)

Coordinating teacher: LINARES LOPEZ, CARLOS

Department assigned to the subject: Department of Computer Science and Engineering

Type: Compulsory
ECTS Credits: 6.0 ECTS


Requirements (Subjects that are assumed to be known)
Programming (Course: 1 / Semester: 1) Algorithms and Data Structures (Course: 1 / Semester: 2) Logic (Course: 1 / Semester: 2) Discrete Mathematics (Course: 1 / Semester: 2) Artificial Intelligence (Course: 2 / Semester: 2)
The objective of this course is to familiarize the student with the fundamental techniques of discrete optimization as well as with the fundamental algorithms for solving satisfiability problems.
Skills and learning outcomes
Link to document

Description of contents: programme
1. Dynamic Programming 2. Linear Programming 3. Boolean Satisfiability 4. Constraints Programming 5. Search
Learning activities and methodology
* Theory lectures: 1 ECTS. They are aimed at reaching the specific cognitive competences of the subject as much as analysis and abstraction. * Practical lectures: 1 ECTS. They are thought to reach the specific practical skills as much as problem resolution and knowledge applicability * Continuous evaluation: 1,5 ECTS. They will start during the practical sessions but should be completed as homework. They are expected to complete both the cognitive competences and the practical skills including problem resolution and knowledge applicability * Final practice: 2 ECTS. To be done without the direct assistance of a professor, they are aimed to complete and integrate all the specific competences and skills by solving two practical cases. The statements of these problems, the resolution method, the results obtained and its interpretation shall be well documented. * Tutoring: TUTORING. Individualized assistance (individual tutoring) or group (collective tutoring) to students by the teacher. * Final exam: 0,5 ECTS. It strengths and complements the development of both the specific cognitive and procedural capacities.
Assessment System
  • % end-of-term-examination 40
  • % of continuous assessment (assigments, laboratory, practicals...) 60
Calendar of Continuous assessment
Basic Bibliography
  • Frederick S. Hiller and Gerald J. Lieberman. Introduction to operations research. McGraw-Hill. 2005
  • Holger Hoos, Thomas Stützle. Stochastic Local Search: Foundations and Applications. Morgan Kaufmann. 2005
  • Jongen, H. Th.. Optimization Theory. Kluwer Academic. 2004
  • Rina Dechter. Constraint Processing. Morgan Kaufmann. 2003
  • Stefan Edelkamp, Stefan Schrödl. Heuristic Search: Theory and Applications. Morgan Kaufmann. 2012
  • Steven S. Skiena. The Algorithm Design Manual. Springer. 2008
  • Sundaram, Rangarajan K.. A first course in optimization theory. Cambridge University Press. 2006
  • Victor W. Marek. Introduction to Mathematics of Solvability. CRC Press. 2009

The course syllabus may change due academic events or other reasons.