1,604 research outputs found
Swap Bribery
In voting theory, bribery is a form of manipulative behavior in which an
external actor (the briber) offers to pay the voters to change their votes in
order to get her preferred candidate elected. We investigate a model of bribery
where the price of each vote depends on the amount of change that the voter is
asked to implement. Specifically, in our model the briber can change a voter's
preference list by paying for a sequence of swaps of consecutive candidates.
Each swap may have a different price; the price of a bribery is the sum of the
prices of all swaps that it involves. We prove complexity results for this
model, which we call swap bribery, for a broad class of election systems,
including variants of approval and k-approval, Borda, Copeland, and maximin.Comment: 17 page
Reconstructing a Simple Polytope from its Graph
Blind and Mani (1987) proved that the entire combinatorial structure (the
vertex-facet incidences) of a simple convex polytope is determined by its
abstract graph. Their proof is not constructive. Kalai (1988) found a short,
elegant, and algorithmic proof of that result. However, his algorithm has
always exponential running time. We show that the problem to reconstruct the
vertex-facet incidences of a simple polytope P from its graph can be formulated
as a combinatorial optimization problem that is strongly dual to the problem of
finding an abstract objective function on P (i.e., a shelling order of the
facets of the dual polytope of P). Thereby, we derive polynomial certificates
for both the vertex-facet incidences as well as for the abstract objective
functions in terms of the graph of P. The paper is a variation on joint work
with Michael Joswig and Friederike Koerner (2001).Comment: 14 page
The Peculiar Phase Structure of Random Graph Bisection
The mincut graph bisection problem involves partitioning the n vertices of a
graph into disjoint subsets, each containing exactly n/2 vertices, while
minimizing the number of "cut" edges with an endpoint in each subset. When
considered over sparse random graphs, the phase structure of the graph
bisection problem displays certain familiar properties, but also some
surprises. It is known that when the mean degree is below the critical value of
2 log 2, the cutsize is zero with high probability. We study how the minimum
cutsize increases with mean degree above this critical threshold, finding a new
analytical upper bound that improves considerably upon previous bounds.
Combined with recent results on expander graphs, our bound suggests the unusual
scenario that random graph bisection is replica symmetric up to and beyond the
critical threshold, with a replica symmetry breaking transition possibly taking
place above the threshold. An intriguing algorithmic consequence is that
although the problem is NP-hard, we can find near-optimal cutsizes (whose ratio
to the optimal value approaches 1 asymptotically) in polynomial time for
typical instances near the phase transition.Comment: substantially revised section 2, changed figures 3, 4 and 6, made
minor stylistic changes and added reference
Extracting dynamical equations from experimental data is NP-hard
The behavior of any physical system is governed by its underlying dynamical
equations. Much of physics is concerned with discovering these dynamical
equations and understanding their consequences. In this work, we show that,
remarkably, identifying the underlying dynamical equation from any amount of
experimental data, however precise, is a provably computationally hard problem
(it is NP-hard), both for classical and quantum mechanical systems. As a
by-product of this work, we give complexity-theoretic answers to both the
quantum and classical embedding problems, two long-standing open problems in
mathematics (the classical problem, in particular, dating back over 70 years).Comment: For mathematical details, see arXiv:0908.2128[math-ph]. v2: final
version, accepted in Phys. Rev. Let
Permissive Controller Synthesis for Probabilistic Systems
We propose novel controller synthesis techniques for probabilistic systems
modelled using stochastic two-player games: one player acts as a controller,
the second represents its environment, and probability is used to capture
uncertainty arising due to, for example, unreliable sensors or faulty system
components. Our aim is to generate robust controllers that are resilient to
unexpected system changes at runtime, and flexible enough to be adapted if
additional constraints need to be imposed. We develop a permissive controller
synthesis framework, which generates multi-strategies for the controller,
offering a choice of control actions to take at each time step. We formalise
the notion of permissivity using penalties, which are incurred each time a
possible control action is disallowed by a multi-strategy. Permissive
controller synthesis aims to generate a multi-strategy that minimises these
penalties, whilst guaranteeing the satisfaction of a specified system property.
We establish several key results about the optimality of multi-strategies and
the complexity of synthesising them. Then, we develop methods to perform
permissive controller synthesis using mixed integer linear programming and
illustrate their effectiveness on a selection of case studies
A Landscape Analysis of Constraint Satisfaction Problems
We discuss an analysis of Constraint Satisfaction problems, such as Sphere
Packing, K-SAT and Graph Coloring, in terms of an effective energy landscape.
Several intriguing geometrical properties of the solution space become in this
light familiar in terms of the well-studied ones of rugged (glassy) energy
landscapes. A `benchmark' algorithm naturally suggested by this construction
finds solutions in polynomial time up to a point beyond the `clustering' and in
some cases even the `thermodynamic' transitions. This point has a simple
geometric meaning and can be in principle determined with standard Statistical
Mechanical methods, thus pushing the analytic bound up to which problems are
guaranteed to be easy. We illustrate this for the graph three and four-coloring
problem. For Packing problems the present discussion allows to better
characterize the `J-point', proposed as a systematic definition of Random Close
Packing, and to place it in the context of other theories of glasses.Comment: 17 pages, 69 citations, 12 figure
On Metric Dimension of Functigraphs
The \emph{metric dimension} of a graph , denoted by , is the
minimum number of vertices such that each vertex is uniquely determined by its
distances to the chosen vertices. Let and be disjoint copies of a
graph and let be a function. Then a
\emph{functigraph} has the vertex set
and the edge set . We study how
metric dimension behaves in passing from to by first showing that
, if is a connected graph of order
and is any function. We further investigate the metric dimension of
functigraphs on complete graphs and on cycles.Comment: 10 pages, 7 figure
Entropy landscape and non-Gibbs solutions in constraint satisfaction problems
We study the entropy landscape of solutions for the bicoloring problem in
random graphs, a representative difficult constraint satisfaction problem. Our
goal is to classify which type of clusters of solutions are addressed by
different algorithms. In the first part of the study we use the cavity method
to obtain the number of clusters with a given internal entropy and determine
the phase diagram of the problem, e.g. dynamical, rigidity and SAT-UNSAT
transitions. In the second part of the paper we analyze different algorithms
and locate their behavior in the entropy landscape of the problem. For instance
we show that a smoothed version of a decimation strategy based on Belief
Propagation is able to find solutions belonging to sub-dominant clusters even
beyond the so called rigidity transition where the thermodynamically relevant
clusters become frozen. These non-equilibrium solutions belong to the most
probable unfrozen clusters.Comment: 38 pages, 10 figure
- …