541 research outputs found
Logics for complexity classes
A new syntactic characterization of problems complete via Turing reductions
is presented. General canonical forms are developed in order to define such
problems. One of these forms allows us to define complete problems on ordered
structures, and another form to define them on unordered non-Aristotelian
structures. Using the canonical forms, logics are developed for complete
problems in various complexity classes. Evidence is shown that there cannot be
any complete problem on Aristotelian structures for several complexity classes.
Our approach is extended beyond complete problems. Using a similar form, a
logic is developed to capture the complexity class which very
likely contains no complete problem.Comment: This article has been accepted for publication in Logic Journal of
IGPL Published by Oxford University Press; 23 pages, 2 figure
Tight Kernel Bounds for Problems on Graphs with Small Degeneracy
In this paper we consider kernelization for problems on d-degenerate graphs,
i.e. graphs such that any subgraph contains a vertex of degree at most .
This graph class generalizes many classes of graphs for which effective
kernelization is known to exist, e.g. planar graphs, H-minor free graphs, and
H-topological-minor free graphs. We show that for several natural problems on
d-degenerate graphs the best known kernelization upper bounds are essentially
tight.Comment: Full version of ESA 201
The Connectivity of Boolean Satisfiability: Computational and Structural Dichotomies
Boolean satisfiability problems are an important benchmark for questions
about complexity, algorithms, heuristics and threshold phenomena. Recent work
on heuristics, and the satisfiability threshold has centered around the
structure and connectivity of the solution space. Motivated by this work, we
study structural and connectivity-related properties of the space of solutions
of Boolean satisfiability problems and establish various dichotomies in
Schaefer's framework.
On the structural side, we obtain dichotomies for the kinds of subgraphs of
the hypercube that can be induced by the solutions of Boolean formulas, as well
as for the diameter of the connected components of the solution space. On the
computational side, we establish dichotomy theorems for the complexity of the
connectivity and st-connectivity questions for the graph of solutions of
Boolean formulas. Our results assert that the intractable side of the
computational dichotomies is PSPACE-complete, while the tractable side - which
includes but is not limited to all problems with polynomial time algorithms for
satisfiability - is in P for the st-connectivity question, and in coNP for the
connectivity question. The diameter of components can be exponential for the
PSPACE-complete cases, whereas in all other cases it is linear; thus, small
diameter and tractability of the connectivity problems are remarkably aligned.
The crux of our results is an expressibility theorem showing that in the
tractable cases, the subgraphs induced by the solution space possess certain
good structural properties, whereas in the intractable cases, the subgraphs can
be arbitrary
Average-Case Complexity
We survey the average-case complexity of problems in NP.
We discuss various notions of good-on-average algorithms, and present
completeness results due to Impagliazzo and Levin. Such completeness results
establish the fact that if a certain specific (but somewhat artificial) NP
problem is easy-on-average with respect to the uniform distribution, then all
problems in NP are easy-on-average with respect to all samplable distributions.
Applying the theory to natural distributional problems remain an outstanding
open question. We review some natural distributional problems whose
average-case complexity is of particular interest and that do not yet fit into
this theory.
A major open question whether the existence of hard-on-average problems in NP
can be based on the PNP assumption or on related worst-case assumptions.
We review negative results showing that certain proof techniques cannot prove
such a result. While the relation between worst-case and average-case
complexity for general NP problems remains open, there has been progress in
understanding the relation between different ``degrees'' of average-case
complexity. We discuss some of these ``hardness amplification'' results
Recognising Multidimensional Euclidean Preferences
Euclidean preferences are a widely studied preference model, in which
decision makers and alternatives are embedded in d-dimensional Euclidean space.
Decision makers prefer those alternatives closer to them. This model, also
known as multidimensional unfolding, has applications in economics,
psychometrics, marketing, and many other fields. We study the problem of
deciding whether a given preference profile is d-Euclidean. For the
one-dimensional case, polynomial-time algorithms are known. We show that, in
contrast, for every other fixed dimension d > 1, the recognition problem is
equivalent to the existential theory of the reals (ETR), and so in particular
NP-hard. We further show that some Euclidean preference profiles require
exponentially many bits in order to specify any Euclidean embedding, and prove
that the domain of d-Euclidean preferences does not admit a finite forbidden
minor characterisation for any d > 1. We also study dichotomous preferencesand
the behaviour of other metrics, and survey a variety of related work.Comment: 17 page
The Complexity of Computing the Size of an Interval
Given a p-order A over a universe of strings (i.e., a transitive, reflexive,
antisymmetric relation such that if (x, y) is an element of A then |x| is
polynomially bounded by |y|), an interval size function of A returns, for each
string x in the universe, the number of strings in the interval between strings
b(x) and t(x) (with respect to A), where b(x) and t(x) are functions that are
polynomial-time computable in the length of x.
By choosing sets of interval size functions based on feasibility requirements
for their underlying p-orders, we obtain new characterizations of complexity
classes. We prove that the set of all interval size functions whose underlying
p-orders are polynomial-time decidable is exactly #P. We show that the interval
size functions for orders with polynomial-time adjacency checks are closely
related to the class FPSPACE(poly). Indeed, FPSPACE(poly) is exactly the class
of all nonnegative functions that are an interval size function minus a
polynomial-time computable function.
We study two important functions in relation to interval size functions. The
function #DIV maps each natural number n to the number of nontrivial divisors
of n. We show that #DIV is an interval size function of a polynomial-time
decidable partial p-order with polynomial-time adjacency checks. The function
#MONSAT maps each monotone boolean formula F to the number of satisfying
assignments of F. We show that #MONSAT is an interval size function of a
polynomial-time decidable total p-order with polynomial-time adjacency checks.
Finally, we explore the related notion of cluster computation.Comment: This revision fixes a problem in the proof of Theorem 9.
Essentially Tight Kernels For (Weakly) Closed Graphs
We study kernelization of classic hard graph problems when the input graphs
fulfill triadic closure properties. More precisely, we consider the recently
introduced parameters closure number and the weak closure number
[Fox et al., SICOMP 2020] in addition to the standard parameter solution size
. For Capacitated Vertex Cover, Connected Vertex Cover, and Induced Matching
we obtain the first kernels of size and , respectively, thus extending previous kernelization
results on degenerate graphs. The kernels are essentially tight, since these
problems are unlikely to admit kernels of size by previous
results on their kernelization complexity in degenerate graphs [Cygan et al.,
ACM TALG 2017]. In addition, we provide lower bounds for the kernelization of
Independent Set on graphs with constant closure number~ and kernels for
Dominating Set on weakly closed split graphs and weakly closed bipartite
graphs
Complexity of Verification in Self-Assembly with Prebuilt Assemblies
We analyze the complexity of two fundamental verification problems within a generalization of the two-handed tile self-assembly model (2HAM) where initial system assemblies are not restricted to be singleton tiles, but may be larger pre-built assemblies. Within this model we consider the producibility problem, which asks if a given tile system builds, or produces, a given assembly, and the unique assembly verification (UAV) problem, which asks if a given system uniquely produces a given assembly. We show that producibility is NP-complete and UAV is coNP^{NP}-complete even when the initial assembly size and temperature threshold are both bounded by a constant. This is in stark contrast to results in the standard model with singleton input tiles where producibility is in P and UAV is in coNP for ?(1) bounded temperature and coNP-complete when temperature is part of the input. We further provide preliminary results for producibility and UAV in the case of 1-dimensional linear assemblies with pre-built assemblies, and provide polynomial time solutions
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