Randomised Backtracking for Linear Pseudo-Boolean Constraint Problems

Many constraint satisfaction and optimisation problems can be expressed using linear constraints on pseudo-Boolean (0/1) variables. Problems expressed in this form are usually solved by integer programming techniques, but good results have also been obtained using generalisations of SAT algorithms based on both backtracking and local search. A recent class of algorithm uses randomised backtracking to combine constraint propagation with local search-like scalability, at the cost of completeness. This paper describes such an algorithm for linear pseudo-Boolean constraint problems. In experiments it compares well with state-ofthe-art algorithms on hardware verification and balanced incomplete block design generation, and finds improved solutions for three instances of the Social Golfer Problem.