61 research outputs found
On Improving Local Search for Unsatisfiability
Stochastic local search (SLS) has been an active field of research in the
last few years, with new techniques and procedures being developed at an
astonishing rate. SLS has been traditionally associated with satisfiability
solving, that is, finding a solution for a given problem instance, as its
intrinsic nature does not address unsatisfiable problems. Unsatisfiable
instances were therefore commonly solved using backtrack search solvers. For
this reason, in the late 90s Selman, Kautz and McAllester proposed a challenge
to use local search instead to prove unsatisfiability. More recently, two SLS
solvers - Ranger and Gunsat - have been developed, which are able to prove
unsatisfiability albeit being SLS solvers. In this paper, we first compare
Ranger with Gunsat and then propose to improve Ranger performance using some of
Gunsat's techniques, namely unit propagation look-ahead and extended
resolution
Solving Linux Upgradeability Problems Using Boolean Optimization
Managing the software complexity of package-based systems can be regarded as
one of the main challenges in software architectures. Upgrades are required on
a short time basis and systems are expected to be reliable and consistent after
that. For each package in the system, a set of dependencies and a set of
conflicts have to be taken into account. Although this problem is
computationally hard to solve, efficient tools are required. In the best
scenario, the solutions provided should also be optimal in order to better
fulfill users requirements and expectations. This paper describes two different
tools, both based on Boolean satisfiability (SAT), for solving Linux
upgradeability problems. The problem instances used in the evaluation of these
tools were mainly obtained from real environments, and are subject to two
different lexicographic optimization criteria. The developed tools can provide
optimal solutions for many of the instances, but a few challenges remain.
Moreover, it is our understanding that this problem has many similarities with
other configuration problems, and therefore the same techniques can be used in
other domains.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
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
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