125 research outputs found
On Existence and Properties of Approximate Pure Nash Equilibria in Bandwidth Allocation Games
In \emph{bandwidth allocation games} (BAGs), the strategy of a player
consists of various demands on different resources. The player's utility is at
most the sum of these demands, provided they are fully satisfied. Every
resource has a limited capacity and if it is exceeded by the total demand, it
has to be split between the players. Since these games generally do not have
pure Nash equilibria, we consider approximate pure Nash equilibria, in which no
player can improve her utility by more than some fixed factor through
unilateral strategy changes. There is a threshold (where
is a parameter that limits the demand of each player on a specific
resource) such that -approximate pure Nash equilibria always exist for
, but not for . We give both
upper and lower bounds on this threshold and show that the
corresponding decision problem is -hard. We also show that the
-approximate price of anarchy for BAGs is . For a restricted
version of the game, where demands of players only differ slightly from each
other (e.g. symmetric games), we show that approximate Nash equilibria can be
reached (and thus also be computed) in polynomial time using the best-response
dynamic. Finally, we show that a broader class of utility-maximization games
(which includes BAGs) converges quickly towards states whose social welfare is
close to the optimum
Approximating Node-Weighted k-MST on Planar Graphs
We study the problem of finding a minimum weight connected subgraph spanning
at least vertices on planar, node-weighted graphs. We give a
(4+\eps)-approximation algorithm for this problem. We achieve this by
utilizing the recent LMP primal-dual -approximation for the node-weighted
prize-collecting Steiner tree problem by Byrka et al (SWAT'16) and adopting an
approach by Chudak et al. (Math.\ Prog.\ '04) regarding Lagrangian relaxation
for the edge-weighted variant. In particular, we improve the procedure of
picking additional vertices (tree merging procedure) given by Sadeghian (2013)
by taking a constant number of recursive steps and utilizing the limited
guessing procedure of Arora and Karakostas (Math.\ Prog.\ '06). More generally,
our approach readily gives a (\nicefrac{4}{3}\cdot r+\eps)-approximation on
any graph class where the algorithm of Byrka et al.\ for the prize-collecting
version gives an -approximation. We argue that this can be interpreted as a
generalization of an analogous result by K\"onemann et al. (Algorithmica~'11)
for partial cover problems. Together with a lower bound construction by Mestre
(STACS'08) for partial cover this implies that our bound is essentially best
possible among algorithms that utilize an LMP algorithm for the Lagrangian
relaxation as a black box. In addition to that, we argue by a more involved
lower bound construction that even using the LMP algorithm by Byrka et al.\ in
a \emph{non-black-box} fashion could not beat the factor \nicefrac{4}{3}\cdot
r when the tree merging step relies only on the solutions output by the LMP
algorithm
Fast Distributed Approximation for Max-Cut
Finding a maximum cut is a fundamental task in many computational settings.
Surprisingly, it has been insufficiently studied in the classic distributed
settings, where vertices communicate by synchronously sending messages to their
neighbors according to the underlying graph, known as the or
models. We amend this by obtaining almost optimal
algorithms for Max-Cut on a wide class of graphs in these models. In
particular, for any , we develop randomized approximation
algorithms achieving a ratio of to the optimum for Max-Cut on
bipartite graphs in the model, and on general graphs in the
model.
We further present efficient deterministic algorithms, including a
-approximation for Max-Dicut in our models, thus improving the best known
(randomized) ratio of . Our algorithms make non-trivial use of the greedy
approach of Buchbinder et al. (SIAM Journal on Computing, 2015) for maximizing
an unconstrained (non-monotone) submodular function, which may be of
independent interest
Approximating k-Forest with Resource Augmentation: A Primal-Dual Approach
In this paper, we study the -forest problem in the model of resource
augmentation. In the -forest problem, given an edge-weighted graph ,
a parameter , and a set of demand pairs , the
objective is to construct a minimum-cost subgraph that connects at least
demands. The problem is hard to approximate---the best-known approximation
ratio is . Furthermore, -forest is as hard to
approximate as the notoriously-hard densest -subgraph problem.
While the -forest problem is hard to approximate in the worst-case, we
show that with the use of resource augmentation, we can efficiently approximate
it up to a constant factor.
First, we restate the problem in terms of the number of demands that are {\em
not} connected. In particular, the objective of the -forest problem can be
viewed as to remove at most demands and find a minimum-cost subgraph that
connects the remaining demands. We use this perspective of the problem to
explain the performance of our algorithm (in terms of the augmentation) in a
more intuitive way.
Specifically, we present a polynomial-time algorithm for the -forest
problem that, for every , removes at most demands and has
cost no more than times the cost of an optimal algorithm
that removes at most demands
Spotting Trees with Few Leaves
We show two results related to the Hamiltonicity and -Path algorithms in
undirected graphs by Bj\"orklund [FOCS'10], and Bj\"orklund et al., [arXiv'10].
First, we demonstrate that the technique used can be generalized to finding
some -vertex tree with leaves in an -vertex undirected graph in
time. It can be applied as a subroutine to solve the
-Internal Spanning Tree (-IST) problem in
time using polynomial space, improving upon previous algorithms for this
problem. In particular, for the first time we break the natural barrier of
. Second, we show that the iterated random bipartition employed by
the algorithm can be improved whenever the host graph admits a vertex coloring
with few colors; it can be an ordinary proper vertex coloring, a fractional
vertex coloring, or a vector coloring. In effect, we show improved bounds for
-Path and Hamiltonicity in any graph of maximum degree
or with vector chromatic number at most 8
A Fixed-Parameter Algorithm for the Max-Cut Problem on Embedded 1-Planar Graphs
We propose a fixed-parameter tractable algorithm for the \textsc{Max-Cut}
problem on embedded 1-planar graphs parameterized by the crossing number of
the given embedding. A graph is called 1-planar if it can be drawn in the plane
with at most one crossing per edge. Our algorithm recursively reduces a
1-planar graph to at most planar graphs, using edge removal and node
contraction. The \textsc{Max-Cut} problem is then solved on the planar graphs
using established polynomial-time algorithms. We show that a maximum cut in the
given 1-planar graph can be derived from the solutions for the planar graphs.
Our algorithm computes a maximum cut in an embedded 1-planar graph with
nodes and edge crossings in time .Comment: conference version from IWOCA 201
Revisiting clustering as matrix factorisation on the Stiefel manifold
International audienceThis paper studies clustering for possibly high dimensional data (e.g. images, time series, gene expression data, and many other settings), and rephrase it as low rank matrix estimation in the PAC-Bayesian framework. Our approach leverages the well known Burer-Monteiro factorisation strategy from large scale optimisation, in the context of low rank estimation. Moreover, our Burer-Monteiro factors are shown to lie on a Stiefel manifold. We propose a new generalized Bayesian estimator for this problem and prove novel prediction bounds for clustering. We also devise a componentwise Langevin sampler on the Stiefel manifold to compute this estimator
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