Checking date: 12/07/2021


Course: 2021/2022

Heuristics and Optimization
(18275)
Study: Bachelor in Applied Mathematics and Computing (362)


Coordinating teacher: LINARES LOPEZ, CARLOS

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

Type: Compulsory
ECTS Credits: 6.0 ECTS

Course:
Semester:




Requirements (Subjects that are assumed to be known)
Programming (Course: 1 / Semester: 1) Algorithms and Data Structures (Course: 1 / Semester: 2) Discrete Mathematics (Course: 2 / Semester: 2) Artificial Intelligence (Course: 2 / Semester: 2)
Skills and learning outcomes
Description of contents: programme
1.- Dynamic programming 2.- Linear programming 3.- Constrained boolean satisfiability 4.- Constraints programming 5.- Search
Learning activities and methodology
LEARNING ACTIVITIES AND METHDOLOGY THEORETICAL-PRACTICAL CLASSES. [44 hours with 100% classroom instruction, 1.67 ECTS] Knowledge and concepts students must acquire. Student receive course notes and will have basic reference texts to facilitate following the classes and carrying out follow up work. Students partake in exercises to resolve practical problems and participate in workshops and evaluation tests, all geared towards acquiring the necessary capabilities. TUTORING SESSIONS. [4 hours of tutoring with 100% on-site attendance, 0.15 ECTS] Individualized attendance (individual tutoring) or in-group (group tutoring) for students with a teacher. STUDENT INDIVIDUAL WORK OR GROUP WORK [98 hours with 0 % on-site, 3.72 ECTS] WORKSHOPS AND LABORATORY SESSIONS [8 hours with 100% on site, 0.3 ECTS] FINAL EXAM. [4 hours with 100% on site, 0.15 ECTS] Global assessment of knowledge, skills and capacities acquired throughout the course. METHODOLOGIES THEORY CLASS. Classroom presentations by the teacher with IT and audiovisual support in which the subject's main concepts are developed, while providing material and bibliography to complement student learning. PRACTICAL CLASS. Resolution of practical cases and problem, posed by the teacher, and carried out individually or in a group. TUTORING SESSIONS. Individualized attendance (individual tutoring sessions) or in-group (group tutoring sessions) for students with a teacher as tutor. LABORATORY PRACTICAL SESSIONS. Applied/experimental learning/teaching in workshops and laboratories under the tutor's supervision.
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.