scholarly journals A Two Level Local Search for MAX-SAT Problems with Hard and Soft Constraints

2002 ◽  
pp. 603-614 ◽  
Author(s):  
John Thornton ◽  
Stuart Bain ◽  
Abdul Sattar ◽  
Duc Nghia Pham
Keyword(s):  
Local Search ◽  
Soft Constraints ◽  
Hard And Soft Constraints ◽  
Max Sat

Max-SAT formalisms with hard and soft constraints

2011 ◽  
Vol 24 (1) ◽  
pp. 101-103 ◽  
Author(s):  
Josep Argelich
Keyword(s):  
Soft Constraints ◽  
Hard And Soft Constraints ◽  
Max Sat

scholarly journals Learning MAX-SAT from Contextual Examples for Combinatorial Optimisation

2020 ◽  
Vol 34 (04) ◽  
pp. 4493-4500
Author(s):  
Mohit Kumar ◽  
Samuel Kolb ◽  
Stefano Teso ◽  
Luc De Raedt
Keyword(s):  
Artificial Intelligence ◽  
Optimization Problems ◽  
Combinatorial Optimisation ◽  
Limiting Factor ◽  
Soft Constraints ◽  
Combinatorial Optimization Problems ◽  
Hard Constraints ◽  
Benchmark Instances ◽  
Hard And Soft Constraints ◽  
Max Sat

Combinatorial optimization problems are ubiquitous in artificial intelligence. Designing the underlying models, however, requires substantial expertise, which is a limiting factor in practice. The models typically consist of hard and soft constraints, or combine hard constraints with a preference function. We introduce a novel setting for learning combinatorial optimisation problems from contextual examples. These positive and negative examples show – in a particular context – whether the solutions are good enough or not. We develop our framework using the MAX-SAT formalism. We provide learnability results within the realizable and agnostic settings, as well as hassle, an implementation based on syntax-guided synthesis and showcase its promise on recovering synthetic and benchmark instances from examples.

A multi-objective approach to nurse scheduling with both hard and soft constraints

1996 ◽  
Vol 30 (3) ◽  
pp. 183-193 ◽  
Author(s):  
Ilham Berrada ◽  
Jacques A. Ferland ◽  
Philippe Michelon
Keyword(s):  
Soft Constraints ◽  
Nurse Scheduling ◽  
Multi Objective ◽  
Hard And Soft Constraints

scholarly journals A Variable Neighborhood Walksat-Based Algorithm for MAX-SAT Problems

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Noureddine Bouhmala
Keyword(s):  
Local Search ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Search Algorithms ◽  
Boolean Formula ◽  
Local Search Algorithms ◽  
Neighborhood Structure ◽  
Np Complete ◽  
Max Sat ◽  
Almost All

The simplicity of the maximum satisfiability problem (MAX-SAT) combined with its applicability in many areas of artificial intelligence and computing science made it one of the fundamental optimization problems. This NP-complete problem refers to the task of finding a variable assignment that satisfies the maximum number of clauses (or the sum of weights of satisfied clauses) in a Boolean formula. The Walksat algorithm is considered to be the main skeleton underlying almost all local search algorithms for MAX-SAT. Most local search algorithms including Walksat rely on the 1-flip neighborhood structure. This paper introduces a variable neighborhood walksat-based algorithm. The neighborhood structure can be combined easily using any local search algorithm. Its effectiveness is compared with existing algorithms using 1-flip neighborhood structure and solvers such as CCLS and Optimax from the eighth MAX-SAT evaluation.

scholarly journals Laplacian Mesh Deformation

2013 ◽  
Author(s):  
Arnaud Gelas
Keyword(s):  
Mesh Deformation ◽  
Soft Constraints ◽  
Surface Mesh ◽  
Flexible Design ◽  
3D Surface ◽  
Hard And Soft Constraints ◽  
Local Detail

Deforming a 3D surface mesh while preserving its local detail is useful for editing anatomical atlases or for mesh based segmentation. This contribution introduces new classes for performing hard and soft constraints deformation in a flexible design which allows user to switch easily in between Laplacian discretization operators, area weighing and solvers. The usage of these new classes is demonstrated on a sphere.

Chapter 23. MaxSAT, Hard and Soft Constraints

2021 ◽  
Author(s):  
Chu Min Li ◽  
Felip Manyà
Keyword(s):  
Combinatorial Optimization ◽  
Lower Bounds ◽  
Branch And Bound ◽  
Data Structures ◽  
Optimization Problems ◽  
Soft Constraints ◽  
Inference Rules ◽  
Combinatorial Optimization Problems ◽  
Definition Of ◽  
Hard And Soft Constraints

MaxSAT solving is becoming a competitive generic approach for solving combinatorial optimization problems, partly due to the development of new solving techniques that have been recently incorporated into modern MaxSAT solvers, and to the challenge problems posed at the MaxSAT Evaluations. In this chapter we present the most relevant results on both approximate and exact MaxSAT solving, and survey in more detail the techniques that have proven to be useful in branch and bound MaxSAT and Weighted MaxSAT solvers. Among such techniques, we pay special attention to the definition of good quality lower bounds, powerful inference rules, clever variable selection heuristics and suitable data structures. Moreover, we discuss the advantages of dealing with hard and soft constraints in the Partial MaxSAT formalims, and present a summary of the MaxSAT Evaluations that have been organized so far as affiliated events of the International Conference on Theory and Applications of Satisfiability Testing.

Sign in / Sign up

Export Citation Format

Share Document