7,363 research outputs found
Shortest Distances as Enumeration Problem
We investigate the single source shortest distance (SSSD) and all pairs
shortest distance (APSD) problems as enumeration problems (on unweighted and
integer weighted graphs), meaning that the elements -- where
and are vertices with shortest distance -- are produced and
listed one by one without repetition. The performance is measured in the RAM
model of computation with respect to preprocessing time and delay, i.e., the
maximum time that elapses between two consecutive outputs. This point of view
reveals that specific types of output (e.g., excluding the non-reachable pairs
, or excluding the self-distances ) and the order of
enumeration (e.g., sorted by distance, sorted row-wise with respect to the
distance matrix) have a huge impact on the complexity of APSD while they appear
to have no effect on SSSD.
In particular, we show for APSD that enumeration without output restrictions
is possible with delay in the order of the average degree. Excluding
non-reachable pairs, or requesting the output to be sorted by distance,
increases this delay to the order of the maximum degree. Further, for weighted
graphs, a delay in the order of the average degree is also not possible without
preprocessing or considering self-distances as output. In contrast, for SSSD we
find that a delay in the order of the maximum degree without preprocessing is
attainable and unavoidable for any of these requirements.Comment: Updated version adds the study of space complexit
The matching relaxation for a class of generalized set partitioning problems
This paper introduces a discrete relaxation for the class of combinatorial
optimization problems which can be described by a set partitioning formulation
under packing constraints. We present two combinatorial relaxations based on
computing maximum weighted matchings in suitable graphs. Besides providing dual
bounds, the relaxations are also used on a variable reduction technique and a
matheuristic. We show how that general method can be tailored to sample
applications, and also perform a successful computational evaluation with
benchmark instances of a problem in maritime logistics.Comment: 33 pages. A preliminary (4-page) version of this paper was presented
at CTW 2016 (Cologne-Twente Workshop on Graphs and Combinatorial
Optimization), with proceedings on Electronic Notes in Discrete Mathematic
Dynamic Facility Location via Exponential Clocks
The \emph{dynamic facility location problem} is a generalization of the
classic facility location problem proposed by Eisenstat, Mathieu, and Schabanel
to model the dynamics of evolving social/infrastructure networks. The
generalization lies in that the distance metric between clients and facilities
changes over time. This leads to a trade-off between optimizing the classic
objective function and the "stability" of the solution: there is a switching
cost charged every time a client changes the facility to which it is connected.
While the standard linear program (LP) relaxation for the classic problem
naturally extends to this problem, traditional LP-rounding techniques do not,
as they are often sensitive to small changes in the metric resulting in
frequent switches.
We present a new LP-rounding algorithm for facility location problems, which
yields the first constant approximation algorithm for the dynamic facility
location problem. Our algorithm installs competing exponential clocks on the
clients and facilities, and connect every client by the path that repeatedly
follows the smallest clock in the neighborhood. The use of exponential clocks
gives rise to several properties that distinguish our approach from previous
LP-roundings for facility location problems. In particular, we use \emph{no
clustering} and we allow clients to connect through paths of \emph{arbitrary
lengths}. In fact, the clustering-free nature of our algorithm is crucial for
applying our LP-rounding approach to the dynamic problem
The Data Big Bang and the Expanding Digital Universe: High-Dimensional, Complex and Massive Data Sets in an Inflationary Epoch
Recent and forthcoming advances in instrumentation, and giant new surveys,
are creating astronomical data sets that are not amenable to the methods of
analysis familiar to astronomers. Traditional methods are often inadequate not
merely because of the size in bytes of the data sets, but also because of the
complexity of modern data sets. Mathematical limitations of familiar algorithms
and techniques in dealing with such data sets create a critical need for new
paradigms for the representation, analysis and scientific visualization (as
opposed to illustrative visualization) of heterogeneous, multiresolution data
across application domains. Some of the problems presented by the new data sets
have been addressed by other disciplines such as applied mathematics,
statistics and machine learning and have been utilized by other sciences such
as space-based geosciences. Unfortunately, valuable results pertaining to these
problems are mostly to be found only in publications outside of astronomy. Here
we offer brief overviews of a number of concepts, techniques and developments,
some "old" and some new. These are generally unknown to most of the
astronomical community, but are vital to the analysis and visualization of
complex datasets and images. In order for astronomers to take advantage of the
richness and complexity of the new era of data, and to be able to identify,
adopt, and apply new solutions, the astronomical community needs a certain
degree of awareness and understanding of the new concepts. One of the goals of
this paper is to help bridge the gap between applied mathematics, artificial
intelligence and computer science on the one side and astronomy on the other.Comment: 24 pages, 8 Figures, 1 Table. Accepted for publication: "Advances in
Astronomy, special issue "Robotic Astronomy
Guarantees and Limits of Preprocessing in Constraint Satisfaction and Reasoning
We present a first theoretical analysis of the power of polynomial-time
preprocessing for important combinatorial problems from various areas in AI. We
consider problems from Constraint Satisfaction, Global Constraints,
Satisfiability, Nonmonotonic and Bayesian Reasoning under structural
restrictions. All these problems involve two tasks: (i) identifying the
structure in the input as required by the restriction, and (ii) using the
identified structure to solve the reasoning task efficiently. We show that for
most of the considered problems, task (i) admits a polynomial-time
preprocessing to a problem kernel whose size is polynomial in a structural
problem parameter of the input, in contrast to task (ii) which does not admit
such a reduction to a problem kernel of polynomial size, subject to a
complexity theoretic assumption. As a notable exception we show that the
consistency problem for the AtMost-NValue constraint admits a polynomial kernel
consisting of a quadratic number of variables and domain values. Our results
provide a firm worst-case guarantees and theoretical boundaries for the
performance of polynomial-time preprocessing algorithms for the considered
problems.Comment: arXiv admin note: substantial text overlap with arXiv:1104.2541,
arXiv:1104.556
Grad and Classes with Bounded Expansion II. Algorithmic Aspects
Classes of graphs with bounded expansion are a generalization of both proper
minor closed classes and degree bounded classes. Such classes are based on a
new invariant, the greatest reduced average density (grad) of G with rank r,
∇r(G). These classes are also characterized by the existence of several
partition results such as the existence of low tree-width and low tree-depth
colorings. These results lead to several new linear time algorithms, such as an
algorithm for counting all the isomorphs of a fixed graph in an input graph or
an algorithm for checking whether there exists a subset of vertices of a priori
bounded size such that the subgraph induced by this subset satisfies some
arbirtrary but fixed first order sentence. We also show that for fixed p,
computing the distances between two vertices up to distance p may be performed
in constant time per query after a linear time preprocessing. We also show,
extending several earlier results, that a class of graphs has sublinear
separators if it has sub-exponential expansion. This result result is best
possible in general
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