Each semester we host one to two visitors. This semester's visitors are still to be determined. Check the list of previous visitors.

Class schedule: the page will be regularly updated. Check it out regularly for announcements of required/recommended reading material, homework, etc.

General Information

Prereq: Instructor's permission

Course description: Constraint satisfaction has emerged as a powerful approach to articulate and solve many problems in computer science, engineering, and management.  It is now the basis for new programming languages and innovative commercial systems for production scheduling, product configuration, personnel planning and timetabling, etc.  The goal of this course is to prepare students to conduct research in this area. The course will be intensive and will require thorough study of the theory and the algorithms, and a significant implementation effort. Students are expected to be self motivated, and demonstrate intellectual independence and collegial collaboration.

Lectures: Monday, Wednesday, Friday, 3:30 p.m. to 4:20 p.m.
Location: AvH 118.

Make-up Class/Recitation: Monday, from 5:00--5:50 4:30--5:20 p.m.
Location: AvH 118.

Instructor:   Prof.  Berthe Y. Choueiry
      Office location: Room 360, Avery Hall,
      choueiry AT, tel: (402)472-5444.
      Office hours: Wednesday/Friday 4:30-5:30 p.m. or by appointment.

GTA: Mr. Robert Woodward
      rwoodwar AT cse . unl . edu
      Office hours: Thursdays, 5:00-6:00 P.M., held in Avery Hall 13A (Student Resource Center) or by appointment.

"Constraint Processing" by Rina Dechter, 1st edition. The textbook will not be followed sequentially, but should be used for reference.

For a quick response, send your questions to Piazza. Your message will be read by the TA and the instructor and they will respond to you ASAP.

Topics include but are not restricted to:

  • Properties, computational complexity, and practical importance.
  • Global and local consistency: algorithms, properties and computational complexity.
  • Islands of tractability for minimality and global consistency.
  • Intelligent backtracking.
  • Look-ahead techniques.
  • Ordering heuristics.
  • Theoretical and empirical comparison of hybrid search algorithms.
  • Phase transition.
  • Modeling and reduction methods between representations.
  • If time permits: Decomposition. Symmetries and their approximations. Temporal constraint networks. Stochastic search. Reformulation and abstraction. Dynamic/conditional Constraint Satisfaction (CSP). Constrained Optimization Problem (COP).
  • Other, depending on class interests.


Last modified: Wed Aug 22 15:10:35 CDT 2012