MIAO Video Seminars
The
MIAO video seminars
are what we call the series of seminars arranged by the
Mathematical
Insights into Algorithms for Optimization (MIAO)
research group
at the University of Copenhagen and Lund University.
These seminars are typically fairly lowkey, informal events, but
they are usually open to the public and are announced beforehand on
our seminar mailing lists (with two different lists for more
theoretically oriented or more applied seminars, respectively). In
case you want to receive the announcements, please just let us know
by sending an email message to jakob.nordstrom@cs.lth.se and we
will add you to the relevant list(s).
In response to multiple requests, since the spring
of 2021 we have started posting most seminars on the
MIAO
Research YouTube channel.
Upcoming video seminars
It seems that the MIAO video seminars have mostly switched to
vacation mode, but we plan to be back in late August. Please note that the dates below are still a bit tentative, though.
All times below are in the Central European time zone (with or without daylight savings as appropriate).

Friday Aug 27 at 10:00
Title TBD
(Dmitriy Traytel, University of Copenhagen)

Monday Sep 6 at 14:00
Lifting with sunflowers
(Ian Mertz, University of Toronto)
Lifting theorems are an important class of techniques which
can transform functions which are hard for weak computation
models into functions which are hard for strong computation
models. Starting with Raz and McKenzie (1999), there has been
a flurry of work proving querytocommunication lifting
theorems, which translate lower bounds on query complexity to
lower bounds for the corresponding communication model. In
this talk I will present a simplified proof of deterministic
querytocommunication lifting. Our proof uses elementary
counting together with a novel connection to the sunflower
lemma.

Monday Sep 13 at 14:00
Title TBD
(Sajin Koroth, Simon Fraser University)

Friday Sep 17
Title TBD
(Kuldeep S. Meel, National University of Singapore)

Monday Sep 20 at 14:00
The KRW conjecture
(Or Meir, University of Haifa)
Proving superlogarithmic lower bounds on the depth of
circuits is one of the main frontiers of circuit
complexity. In 1991, Karchmer, Raz and Wigderson observed that
we could resolve this question by proving the following
conjecture: Given two boolean functions f,g, the depth
complexity of their composition is about the sum of their
individual depth complexities. While we cannot prove the
conjecture yet, there has been some exciting progress toward
such a proof, some of it in the last few years. In this talk,
I will survey the known results and propose future directions
for research on the KRW conjecture.

Monday Sep 27 at 14:00
Title TBD
(Arkadev Chattopadhyay, Tata Institute of Fundamental Research, Mumbai)

Friday Oct 1 at 14:00
Title TBD
(Mika Göös, EPFL)

Friday Oct 15 at 10:00
Logicbased explainable AI
(Alexey Ignatiev, Monash University)

Date and time TBD
Automating algebraic proof systems is NPhard
(Susanna F. de Rezende,
Institute of Mathematics of the Czech Academy of Sciences)
Intuitively, automatability addresses the proof search
problem: How hard is it to find a proof? In this talk, we will
show that algebraic proofs are hard to find: Given an
unsatisfiable CNF formula F, it is NPhard to find a
refutation of F in the Nullstellensatz, Polynomial Calculus,
or SheraliAdams proof systems in time polynomial in the size
of the shortest such refutation. We will first present a
simplified proof of the recent breakthrough of Atserias and
Müller (FOCS 2019) that established an analogous result for
Resolution and then explain how we generalise it to algebraic
proof systems.
This talk is based on joint work with Mika Göös, Jakob
Nordström, Toniann Pitassi, Robert Robere, and Dmitry Sokolov.
Video seminars spring 2021
During the spring semester of 2021,
we had online virtual seminars running every weekday
at 17:30 CET from early February to midMay
as part of the
Satisfiability: Theory, Practice, and Beyond
and
Theoretical Foundations of Computer Systems
programs
at the
Simons Institute
for the Theory of Computing.
On top of this, we had some additional seminars as listed
below.

Monday Jun 7 at 14:00
Proof complexity meets finite model theory
(video)
(Joanna Ochremiak, LaBRI, Université de Bordeaux and CNRS)
Finite model theory studies the power of logics on the class of finite
structures. One of its goals is to characterize symmetric computation,
that is, computation that abstracts away details which are not essential
for the given task, by respecting the symmetries of the input.
In this talk I will discuss connections between proof complexity and
finite model theory, focussing on lower bounds. For certain proof
systems, the existence of a succinct refutation can be decided in a
symmetrypreserving way. This allows us to transfer lower bounds from
finite model theory to proof complexity. I will introduce this approach
and explain its key technical ideas such as the method of folding for
dealing with symmetries in linear programs.

Friday Jun 4 at 14:00
Abstract cores in implicit hitting set based MaxSAT solving
(video)
(Jeremias Berg, University of Helsinki)
Maximum satisfiability (MaxSat) solving is an active area of research
motivated by numerous successful applications to solving NPhard
combinatorial optimization problems. One of the most successful
approaches for solving MaxSat instances from real world domains are the
so called implicit hitting set (IHS) solvers. IHS solvers decouple
MaxSat solving into separate coreextraction and optimization steps
which are tackled by an Boolean satisfiability (SAT) and an integer
linear programming (IP) solver, respectively. While the approach shows
stateoftheart performance on many industrial instances, it is known
that there exists instances on which IHS solvers need to extract an
exponential number of cores before terminating. In this talk I will
present the simplest of these problematic instances
and talk about how abstract cores, a compact representation of a large
number of regular cores that we recently proposed, addresses perhaps the
main bottleneck of IHS solvers. I will show how to incorporate abstract
core reasoning into the IHS algorithm and demonstrate that
that including abstract cores into a stateofthe art IHS solver
improves its performance enough to surpass the best performing solvers
of the recent MaxSat Evaluations.

Friday May 28 at 14:00
Feasible interpolation for algebraic proof systems
(video)
(Tuomas Hakoniemi,
Universitat Politècnica de Catalunya)
In this talk we present a form of feasible interpolation for two
algebraic proof systems, Polynomial Calculus and SumsofSquares. We
show that for both systems there is a polytime algorithm that given two
sets P(x,z) and Q(y,z) of polynomial equalities, a refutation of the union of P(x,z) and Q(y,z) and an assignment a to the zvariables outputs either a refutation of P(x,a) or a refutation of Q(y,a).
Our proofs are fairly logical in nature, in that they rely heavily on
semantic characterizations of resourcebounded refutations to prove the
existence of suitable refutations. These semantic existence proofs
narrow down the search space for the refutations we are after so that we
can actually find them efficiently.

Monday May 17 at 14:00
Averagecase perfect matching lower bounds
from hardness of Tseitin formulas
(video)
(Kilian Risse, KTH Royal Institute of Technology)
We study the complexity of proving that a sparse random regular
graph on an odd number of vertices does not have a perfect matching,
and related problems involving each vertex being matched some
prespecified number of times.
We show that this requires proofs of degree Ω(n / log n) in
the Polynomial Calculus (over fields of characteristic ≠ 2) and
SumofSquares proof systems, and exponential size in the
boundeddepth Frege proof system.
This resolves a question by Razborov asking whether
the LovÃ¡szSchrijver proof system requires n^{δ}
rounds to refute these formulas for some δ > 0.
The results are obtained by a
worstcase to averagecase reduction of these formulas relying on a
topological embedding theorem which may be of independent interest.
Joint work with with Per Austrin.

Monday May 10 at 14:00
On the complexity of branch and cut
(video)
(Noah Fleming, University of Toronto)
The Stabbing Planes proof system was introduced to model practical
branchandcut integer programming solvers. As a proof system, Stabbing
Planes can be viewed as a simple generalization of DPLL to reason about
linear inequalities. It is powerful enough to simulate Cutting Planes
and produce short refutations of the Tseitin formulas — certain
unsatisfiable systems of linear equations mod 2 — which are canonical
hard examples for many algebraic proof systems. In a surprising recent
result, Dadush and Tiwari showed that these short Stabbing Planes
refutations of the Tseitin formulas could be translated into Cutting
Planes proofs. This raises the question of whether all Stabbing Planes
can be efficiently translated into Cutting Planes. In recent work, we
give a partial answer to this question.
In this talk I will introduce and motivate the Stabbing Planes proof
system. I will then show how Stabbing Planes proofs with
quasipolynomially bounded coefficients can be quasipolynomially
translated into Cutting Planes. As a consequence of this translation, we
can show that Cutting Planes has quasipolynomial size refutations of
any unsatisfiable system of linear equations over a finite field. A
remarkable property of our translation for systems of linear equations
over finite fields, and the translation of Dadush and Tiwari for the
Tseitin formulas, is that the resulting proofs are also
quasipolynomially deep. A natural question is thus whether these depth
bounds can be improved. In the last part of the talk, I will discuss
progress towards answering this question in the form of a new depth
lower bound technique for Cutting Planes, which works also for the
stronger semantic Cutting Planes system, and which allows us to
establish the first linear lower bounds on the depth of semantic Cutting
Planes refutations of the Tseitin formulas.

Monday May 3 at 14:00
SATencodings and scalability
(video)
(André Schidler, Technische Universität Wien)
Boolean satisfiability (SAT) solvers have reached stunning performance
over the last decade. This performance can be harnessed for other
problems by means of SAT encodings: translating the problem instance
into a SAT instance. SAT encodings have been used successfully for many
combinatorial problems and are established in industry. In this talk, I
discuss the general idea behind SAT encodings and two specific
application areas: graph decompositions and machine learning.
Graph decompositions allow for specialized algorithms that can solve
hard problems efficiently. SAT encodings provide not only the means to
optimally compute these decompositions, but can be easily extended by
extra constraints specific to the hard problem to be solved.
For machine learning, SAT encodings gained increased attention in
recent years, as they allow the induction of very small AI models.
Learning small models has become more important in the context of
explainable AI: smaller models are usually easier to understand for
humans. We will focus specifically on decision tree induction.
Scalability is one of the main issues when using SAT encodings. We can
overcome this problem by embedding SATbased solving into a heuristic
framework. This allows us to trade off a slightly worse result for large
applicability. In the last part of our talk, we discuss our SATbased
local improvement framework that implements this idea.

Wednesday Apr 28 at 15:00
Slicing the hypercube is not easy
(video)
(Amir Yehudayoff, Technion – Israel Institute of Technology)
How many hyperplanes are needed to slice all edges of the hypercube?
This question has been studied in machine learning, geometry and
computational complexity since the 1970s. We shall describe (most of) an
argument showing that more than n^{0.57} hyperplanes are needed, for large n. We shall also see a couple of applications. This is joint work with Gal Yehuda.

Monday Apr 26 at 14:00
Recent lower bounds in algebraic complexity theory
(video)
(Ben Lee Volk, University of Texas at Austin)
Algebraic complexity theory studies the complexity of solving algebraic
computational tasks using algebraic models of computation. One major
problem in this area is to prove lower bounds on the number of
arithmetic operations required for computing explicit polynomials. This
natural mathematical problem is the algebraic analog of the famous P vs. NP problem. It also has tight connections to other classical mathematical areas and to fundamental questions in complexity theory.
In this talk I will provide background and then present some recent
progress on proving lower bounds for models of algebraic computation,
such as the algebraic analogs of NC^{1} and NL.
Based on joint works with Prerona Chatterjee, Mrinal Kumar, and Adrian She.

Friday Apr 23 at 14:00
On the complexity of branching proofs
(video)
(Daniel Dadush, CWI)
We consider the task of proving integer infeasibility of a bounded
convex K in R^{n} using a general branching
proof system. In a general branching proof, one constructs a branching
tree by adding an integer disjunction ax ⩽ b or
ax ⩾ b+1, for an integer vector a and an
integer b, at each node, such that the leaves of the tree
correspond to empty sets (i.e., K together with the
inequalities picked up from the root to leaf is empty). Recently,
Beame et al (ITCS 2018), asked whether the bit size of the
coefficients in a branching proof, which they named stabbing planes
(SP) refutations, for the case of polytopes derived from SAT formulas,
can be assumed to be polynomial in n. We resolve this
question by showing that any branching proof can be recompiled so that
the integer disjunctions have coefficients of size at most
(nR)^{O(n2)}, where R is the radius of an
l_{1} ball containing K, while increasing
the number of nodes in the branching tree by at most a factor
O(n). As our second contribution, we show that Tseitin
formulas, an important class of infeasible SAT instances, have
quasipolynomial sized cutting plane (CP) refutations, disproving the
conjecture that Tseitin formulas are (exponentially) hard for CP. As
our final contribution, we give a simple family of polytopes in
[0,1]^{n} requiring branching proofs of length
2^{n}/n.
Joint work with Samarth Tiwari.

Monday Apr 19 at 14:00
Kidney exchange programmes —
saving lives with optimisation algorithms
(video)
(William Pettersson, University of Glasgow)
Kidney Exchange Programmes (KEPs) increase the rate of living
donor kidney transplantation, in turn saving lives. In this
talk, I will explain exactly what KEPs are, how KEPs achieve
this goal, and the role of optimisation algorithms within
KEPs. This will include brief explanations of some of the
models used for kidney exchange programmes, some of the more
generic optimisation techniques that we use, as well as recent
advances and specific research directions for the field into
the future. This talk will very much be an overview of kidney
exchange, without going into complex details of optimisation
algorithms, making it suitable for a wider audience.

Thursday Apr 8 at 14:00
(Semi)Algebraic proofs over {±1} variables
(video)
(Dmitry Sokolov, St.Petersburg State University)
One of the major open problems in proof complexity is to prove
lower bounds on AC0[p]Frege proof systems. As a step toward
this goal Impagliazzo, Mouli and Pitassi in a recent paper
suggested proving lower bounds on the size for Polynomial
Calculus over the {±1} basis. In this talk we show a
technique for proving such lower bounds and moreover, we also
give lower bounds on the size for SumofSquares over the
{±1} basis.
We discuss the difference between {0, 1} and {+1, 1} cases and
problems in further generalizations of the lower bounds.

Courtesy of Zuse Institute Berlin:
Friday Feb 12 at 14:00
Introduction to IP presolving techniques in PaPILO
(Alexander Hoen, Zuse Institute Berlin)
Presolving is an essential part contributing to the performance of
modern MIP solvers. PaPILO, a new C++ library, provides presolving
routines for MIP and LP problems. This talk will give an introduction
to basic IPpresolving techniques and provide insights into important
design choices enabling PaPILO's capabilities, in particular
regarding its use of parallel hardware. While presolving itself is
designed to be fast, this can come at the cost of failing to find
important reductions due to working limits and heuristic filtering.
Yet, even most commercial solvers do not use multithreading for the
preprocessing step as of today. PaPILO's design facilitates use of
parallel hardware to allow for more aggressive presolving and
presolving of huge problems. The architecture of PaPILO allows
presolvers generally to run in parallel without requiring expensive
copies of the problem and without special synchronization in the
presolvers themselves. Additionally, the use of Intel's TBB library
aids PaPILO to efficiently exploit recursive parallelism within
expensive presolving routines, such as probing, dominated columns, or
constraint sparsification. Despite PaPILO's use of parallelization,
its results are guaranteed to be deterministic independently of the
number of threads available.
Video seminars autumn 2020

Friday Oct 9 at 13:15
Model counting with probabilistic component caching
(Shubham Sharma and Kuldeep S. Meel,
National University of Singapore)
Given a Boolean formula F, the problem of model counting, also
referred to as #SAT, seeks to compute the number of solutions of F.
Model counting is a fundamental problem with a wide variety of
applications ranging from planning, quantified information flow to
probabilistic reasoning and the like. The modern #SAT solvers tend to
be either based on static decomposition, dynamic decomposition, or a
hybrid of the two. Despite dynamic decomposition based #SAT solvers
sharing much of their architecture with SAT solvers, the core design
and heuristics of dynamic decompositionbased #SAT solvers has
remained constant for over a decade. In this paper, we revisit the
architecture of the stateoftheart dynamic decompositionbased #SAT
tool, sharpSAT, and demonstrate that by introducing a new notion of
probabilistic component caching and the usage of universal hashing
for exact model counting along with the development of several new
heuristics can lead to significant performance
improvement over stateoftheart modelcounters. In particular, we
develop GANAK, a new scalable probabilistic exact model counter that
outperforms stateoftheart exact and approximate model counters for
a wide variety of instances.

Friday Sep 25 at 13:15
Manthan: A datadriven approach for Boolean function synthesis
(Priyanka Golia and Kuldeep S. Meel,
National University of Singapore)
Boolean functional synthesis is a fundamental problem in computer
science with wideranging applications and has witnessed a surge of
interest resulting in progressively improved techniques over the past
decade. Despite intense algorithmic development, a large number of
problems remain beyond the reach of the current stateoftheart
techniques. Motivated by the progress in machine learning, we propose
Manthan, a novel datadriven approach to Boolean functional
synthesis. Manthan views functional synthesis as a classification
problem, relying on advances in constrained sampling for data
generation, and advances in automated reasoning for a novel
proofguided refinement and provable verification. On an extensive
and rigorous evaluation over 609 benchmarks, we demonstrate that
Manthan significantly improves upon the current state of the art,
solving 356 benchmarks in comparison to 280, which is the most solved
by a stateoftheart technique; thereby, we demonstrate an increase
of 76 benchmarks over the current state of the art. The significant
performance improvements, along with our detailed analysis, highlights
several interesting avenues of future work at the intersection of
machine learning, constrained sampling, and automated reasoning.

Friday Aug 28 at 13:15
Tuning Sat4j PB solvers for decision problems
(Romain Wallon,
Université d'Artois)
During the last decades, many improvements in CDCL SAT solvers have
made possible to solve efficiently large problems containing millions of
variables and clauses.
Despite this practical efficiency, some instances remain out of reach
for such solvers.
This is particularly true when the input formula requires an
exponential size refutation proof in the resolution proof system (as for
pigeonhole formulae).
This observation motivated the development of another kind of solvers,
known as pseudoBoolean (PB) solvers.
These solvers take as inputs a conjunction of PB constraints (integral
linear inequations over Boolean variables) and benefit from the cutting
planes proof system, which is (in theory) stronger than the resolution
proof system.
To implement this proof system, current PB solvers follow the direction
of modern SAT solvers, by implementing a conflict analysis procedure
relying on the application of cutting planes rules.
However, adapting the CDCL architecture to take into account PB
constraints is not as straightforward as it may look.
In particular, many CDCL invariants and properties do not hold anymore
as long as PB constraints are considered.
While some of them may be safely ignored (e.g., when they affect the
decision heuristic, the deletion strategy or the restart policy), some
others must be fixed to ensure the soundness of the solver (e.g., by
ensuring to preserve the conflict during its analysis).
In this talk, we will give an overview of the main differences between
PB solving and classical SAT solving, and present different approaches
that have been designed to take these differences into account to extend
the CDCL architecture to PB problems.
We will in particular discuss the pros and cons of these approaches,
and we will explain how they can be enhanced to improve the practical
performance of PB solvers.

Friday Aug 21 at 10:15
Using proofs to analyze SAT solvers
(Janne Kokkala,
Lund University and University of Copenhagen)
The main idea of this seminar is to give an overview of what
one can ask and what one can learn about SAT solvers when
analyzing the proof to estimate what part of the work was
useful. I will start by giving a 17ish minute alpha test run
for my presentation for our CP paper [1]. After that, I will
discuss earlier works that use the same or a similar idea for
general insights [2], analyzing VSIDS usefulness [3], and
clause exchange for parallel solvers [4,5]. To conclude, we
can discuss how this approach could be used for pseudoBoolean
solvers.
[1] J. I. Kokkala, J. Nordström.
Using Resolution Proofs to
Analyse CDCL SAT solvers.
[2] L. Simon.
Post Mortem Analysis of SAT Solver Proofs.
[3] S. Malik, V. Ying.
On the efficiency of the VSIDS decision heuristic.
(Workshop presentation.)
[4] G. Audemard, L. Simon.
Lazy
clause exchange policy for parallel SAT solvers.
[5] G. Katsirelos, A. Sabharwal, H. Samulowitz, L. Simon.
Resolution and parallelizability: Barriers to the efficient
parallelization of SAT solvers.

Wednesday Aug 19 at 13:15
On computational aspects of the antibandwidth problem
(Markus Sinnl,
Johannes Kepler University Linz)
In this talk, we consider the antibandwidth problem, also
known as dual bandwidth problem, separation problem and
maximum differential coloring problem. Given a labeled graph
(i.e., a numbering of the vertices of a graph), the
antibandwidth of a node is defined as the minimum absolute
difference of its labeling to the labeling of all its adjacent
vertices. The goal in the antibandwidth problem is to find a
labeling maximizing the antibandwidth. The problem is NPhard
in general graphs and has applications in diverse areas like
scheduling, radio frequency assignment, obnoxious facility
location and mapcoloring.
There has been much work on deriving theoretical bounds for
the problem and also in the design of metaheuristics in recent
years. However, the optimality gaps between the best known
solution values and reported upper bounds for the
HarwellBoeing Matrixinstances, which are the commonly used
benchmark instances for this problem, were often very large
(e.g., up to 577%).
We present new mixedinteger programming approaches for the
problem, including one approach, which does not directly
formulate the problem as optimization problem, but as a series
of feasibility problems. We also discuss how these feasibility
problems can be encoded with various SATencodings, including
a new and specialised encoding which exploits a certain
staircasestructure occuring in the problem formulation. We
present computational results for all the algorithms,
including a comparison of the MIP and SATapproaches. Our
developed approaches allow to find the proven optimal solution
for eight instances from literature, where the optimal
solution was unknown and also provide reduced gaps for eleven
additional instances, including improved solution values for
seven instances, the largest optimality gap is now
46%. Instances based on the problem were submitted to the SAT
Competition 2020.
Video seminars spring 2020

Wednesday Jul 15 at 15:00
Naïve algorithm selection for SAT solving
(Stephan Gocht,
Lund University and University of Copenhagen)
Although solving propositional formulas is an NPcomplete
problem, stateoftheart SAT solvers are able to solve formulas
with millions of variables. To obtain good performance it is
necessary to configure parameters for heuristic
decisions. However, there is no single parameter configuration
that is perfect for all formulas, and choosing the parameters is
a difficult task. The standard approach is to evaluate different
configurations on some formulas and to choose the single
configuration, that performs best overall. This configuration,
which is called single best solver, is then used to solve new
unseen formulas. In this paper we demonstrate how random forests
can be used to choose a configuration dynamically based on
simple features of the formula. The evaluation shows that our
approach is able to outperform the single best solver on
formulas that are similar to the training set, but not on
formulas from completely new domains.

Friday Jun 26 at 10:00
Behind the scenes of chronological CDCL
(Sibylle Möhle and Armin Biere,
Johannes Kepler University Linz)
Combining conflictdriven clause learning (CDCL) with
chronological backtracking is challenging: Multiple invariants
considered crucial in modern SAT solvers are violated, if
after conflict analysis the solver does not jump to the
assertion level but to a higher decision level instead. In
their SAT'18 paper "Chronological Backtracking", Alexander
Nadel and Vadim Ryvchim provide a fix to this issue. Moreover,
their SAT solver implementing chronological backtracking won
the main track of the SAT Competition 2018. In our SAT'19
paper, "Backing Backtracking", we present a formalization and
generalization of this method. We prove its correctness and
provide an independent implementation.
In this seminar, we demonstrate the working of chronological
CDCL by means of an example. In this example, after a conflict
the conflicting clause contains only one literal at conflict
level. It is therefore used as a reason for backtracking, thus
saving the effort of conflict analysis. We further show which
invariants are violated and present new ones followed by a
discussion of the rules of our formal framework. We also shed
light onto implementation details, including those which are
not mentioned in our SAT'19 paper.

Monday Jun 22 at 13:30
McSplit: A partitioning algorithm for maximum common subgraph
problems
(Ciaran McCreesh and James Trimble,
University of Glasgow)
We will give a short introduction to McSplit, an algorithm for
the maximum common (connected) subgraph problem coauthored
with Patrick Prosser and presented at IJCAI 2017. McSplit
resembles a forwardchecking constraint programming algorithm,
but uses a partitioning data structure to store domains which
greatly reduces memory use and time per search node. We will
also present our recent work with Stephan Gocht and Jakob
Nordström on adding proof logging to the algorithm, turning
McSplit into a certifying algorithm whose outputs can be
independently verified.

Thursday Jun 18 at 20:30
A pseudoBoolean approach to nonlinear verification
(Vincent Liew,
University of Washington)
We discuss some new experimental results showing the promise
of using pseudoBoolean solvers, rather than SAT solvers, to
verify bitvector problems containing multiplication. We use
this approach to efficiently verify the commutativity of a
multiplier output bit by output bit. We also give some examples
of simple bitvector inequalities where the pseudoBoolean
approach significantly outperformed SAT solvers and even
bitvector solvers. Finally, we give some of our observations on
the strengths and weaknesses of different methods of conflict
analysis used by pseudoBoolean solvers.

Friday May 8 at 13:15
PseudoBoolean solvers for answer set programming
(Bart Bogaerts, Vrije Universiteit Brussel)
Answer set programming (ASP) is a wellestablished knowledge
representation formalism. Most ASP solvers are based on
(extensions of) technology from Boolean satisfiability
solving. While these solvers have shown to be very successful in
many practical applications, their strength is limited by their
underlying proof system, resolution. In this research, we
present a new tool LP2PB that translates ASP programs into
pseudoBoolean theories, for which solvers based on the
(stronger) cutting plane proof system exist. We evaluate our
tool, and the potential of cuttingplanebased solving for ASP
on traditional ASP benchmarks as well as benchmarks from
pseudoBoolean solving. Our results are mixed: overall,
traditional ASP solvers still outperform our translational
approach, but several benchmark families are identified where
the balance shifts the other way, thereby suggesting that
further investigation into a stronger proof system for ASP is
valuable.
