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Max-Sur-CSP on Two Elements
Linköping University, Department of Computer and Information Science, Software and Systems. Linköping University, The Institute of Technology. (TCSLAB)
2012 (English)In: Principles and Practice of Constraint Programming 18th International Conference, CP 2012, Québec City, QC, Canada, October 8-12, 2012. Proceedings / [ed] Michela Milano, Springer Berlin Heidelberg , 2012, 38-54 p.Chapter in book (Refereed)
Abstract [en]

Max-Sur-CSP is the following optimisation problem: given a set of constraints, find a surjective mapping of the variables to domain values that satisfies as many of the constraints as possible. Many natural problems, e.g. Minimum k-Cut (which has many different applications in a variety of fields) and Minimum Distance (which is an important problem in coding theory), can be expressed as Max-Sur-CSPs. We study Max-Sur-CSP on the two-element domain and determine the computational complexity for all constraint languages (families of allowed constraints). Our results show that the problem is solvable in polynomial time if the constraint language belongs to one of three classes, and NP-hard otherwise. An important part of our proof is a polynomial-time algorithm for enumerating all near-optimal solutions to a generalised minimum cut problem. This algorithm may be of independent interest.

Place, publisher, year, edition, pages
Springer Berlin Heidelberg , 2012. 38-54 p.
Lecture Notes in Computer Science, ISSN 0302-9743 (print), 1611-3349 (online) ; 7514
National Category
Computer Science
URN: urn:nbn:se:liu:diva-79515DOI: 10.1007/978-3-642-33558-7_6ISBN: 978-3-642-33557-0 (print)ISBN: 978-3-642-33558-7 (online)OAI: diva2:543175
18th International Conference on Principles and Practice of Constraint Programming (CP-2012), 8-12 October, Québec City, Canada
Available from: 2012-08-06 Created: 2012-08-06 Last updated: 2015-04-23Bibliographically approved
In thesis
1. On Some Combinatorial Optimization Problems: Algorithms and Complexity
Open this publication in new window or tab >>On Some Combinatorial Optimization Problems: Algorithms and Complexity
2015 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

This thesis is about the computational complexity of several classes of combinatorial optimization problems, all related to the constraint satisfaction problems.

A constraint language consists of a domain and a set of relations on the domain. For each such language there is a constraint satisfaction problem (CSP). In this problem we are given a set of variables and a collection of constraints, each of which is constraining some variables with a relation in the language. The goal is to determine if domain values can be assigned to the variables in a way that satisfies all constraints. An important question is for which constraint languages the corresponding CSP can be solved in polynomial time. We study this kind of question for optimization problems related to the CSPs.

The main focus is on extended minimum cost homomorphism problems. These are optimization versions of CSPs where instances come with an objective function given by a weighted sum of unary cost functions, and where the goal is not only to determine if a solution exists, but to find one of minimum cost. We prove a complete classification of the complexity for these problems on three-element domains. We also obtain a classification for the so-called conservative case.

Another class of combinatorial optimization problems are the surjective maximum CSPs. These problems are variants of CSPs where a non-negative weight is attached to each constraint, and the objective is to find a surjective mapping of the variables to values that maximizes the weighted sum of satisfied constraints. The surjectivity requirement causes these problems to behave quite different from for example the minimum cost homomorphism problems, and many powerful techniques are not applicable. We prove a dichotomy for the complexity of the problems in this class on two-element domains. An essential ingredient in the proof is an algorithm that solves a generalized version of the minimum cut problem. This algorithm might be of independent interest.

In a final part we study properties of NP-hard optimization problems. This is done with the aid of restricted forms of polynomial-time reductions that for example preserves solvability in sub-exponential time. Two classes of optimization problems similar to those discussed above are considered, and for both we obtain what may be called an easiest NP-hard problem. We also establish some connections to the exponential time hypothesis.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2015. 32 p.
Linköping Studies in Science and Technology. Dissertations, ISSN 0345-7524 ; 1663
Computational complexity, optimization, constraint satisfaction problem
National Category
Computer Science
urn:nbn:se:liu:diva-116859 (URN)10.3384/diss.diva-116859 (DOI)978-91-7519-072-3 (print) (ISBN)
Public defence
2015-05-21, Alan Turing, E-huset, Campus Valla, Linköping, 13:15 (English)
CUGS (National Graduate School in Computer Science), 09.01
Available from: 2015-04-23 Created: 2015-04-07 Last updated: 2015-04-27Bibliographically approved

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