Comparison of PBO solvers in a dependency solving domain

Linux package managers have to deal with dependencies and conflicts of packages required to be installed by the user. As an NP-complete problem, this is a hard task to solve. In this context, several approaches have been pursued. Apt-pbo is a package manager based on the apt project that encodes the dependency solving problem as a pseudo-Boolean optimization (PBO) problem. This paper compares different PBO solvers and their effectiveness on solving the dependency solving problem.Comment: In Proceedings LoCoCo 2010, arXiv:1007.083

Handling software upgradeability problems with MILP solvers

Upgradeability problems are a critical issue in modern operating systems. The problem consists in finding the "best" solution according to some criteria, to install, remove or upgrade packages in a given installation. This is a difficult problem: the complexity of the upgradeability problem is NP complete and modern OS contain a huge number of packages (often more than 20 000 packages in a Linux distribution). Moreover, several optimisation criteria have to be considered, e.g., stability, memory efficiency, network efficiency. In this paper we investigate the capabilities of MILP solvers to handle this problem. We show that MILP solvers are very efficient when the resolution is based on a linear combination of the criteria. Experiments done on real benchmarks show that the best MILP solvers outperform CP solvers and that they are significantly better than Pseudo Boolean solvers.Comment: In Proceedings LoCoCo 2010, arXiv:1007.083

The first evaluation of pseudo-boolean solvers (PB'05)

The first evaluation of pseudo-boolean solvers was organized as a subtrack of the SAT 2005 competition. The first goal of this event is to take a snapshot of the current state of the art in the field of pseudo-boolean constraints. The second goal is to stimulate the research efforts in this field and contribute to the creation of better technologies. This paper details the organization and the results of this event

Search Pruning Conditions for Boolean Optimization

. This paper proposes new algorithms for the Binate Covering Problem (BCP), a well-known restriction of Boolean Optimization. Binate Covering finds application in many areas of Computer Science and Engineering. In Artificial Intelligence, BCP can be used for computing minimum-size prime implicants of Boolean functions, of interest in Automated Reasoning and Non-Monotonic Reasoning. Moreover, Binate Covering is an essential modeling tool in Electronic Design Automation. The objectives of the paper are to briefly review branchand -bound algorithms for BCP, to describe how to apply backtrack search pruning techniques from the Boolean Satisfiability (SAT) domain to BCP, and to illustrate how to strengthen those pruning techniques by exploiting the actual formulation of BCP. Experimental results, obtained on representative instances indicate that the proposed techniques provide significant performance gains for different classes of instances. 1 Introduction The generic Boolean Optimizati..

On Using Cutting Planes in Pseudo-Boolean Optimization

Cutting planes are a well-known, widely used, and very effective technique for Integer Linear Programming (ILP). However, cutting plane techniques are seldom used in Pseudo-Boolean Optimization (PBO) algorithms. This paper addresses the utilization of Gomory mixed-integer and clique cuts, in Satisfiability-based algorithms for PBO, and shows how these cuts can be used for computing lower bounds and for learning new constraints. A side result of learning new constraints is that the utilization of cutting planes enables nonchronological backtracking. Besides cutting planes, the paper also shows that the utilization of search restarts in PBO can be effective in practice, allowing the computation of tighter lower bounds each time the search restarts. The more aggressive lower bounds result from the constraints learned due to the utilization of cutting planes. Experimental results show that the integration of cutting planes and search restarts in a SAT-based algorithm for PBO yields a competitive new solution for PBO