7,012 research outputs found
On Approximating Four Covering and Packing Problems
In this paper, we consider approximability issues of the following four
problems: triangle packing, full sibling reconstruction, maximum profit
coverage and 2-coverage. All of them are generalized or specialized versions of
set-cover and have applications in biology ranging from full-sibling
reconstructions in wild populations to biomolecular clusterings; however, as
this paper shows, their approximability properties differ considerably. Our
inapproximability constant for the triangle packing problem improves upon the
previous results; this is done by directly transforming the inapproximability
gap of Haastad for the problem of maximizing the number of satisfied equations
for a set of equations over GF(2) and is interesting in its own right. Our
approximability results on the full siblings reconstruction problems answers
questions originally posed by Berger-Wolf et al. and our results on the maximum
profit coverage problem provides almost matching upper and lower bounds on the
approximation ratio, answering a question posed by Hassin and Or.Comment: 25 page
Selfish Bin Covering
In this paper, we address the selfish bin covering problem, which is greatly
related both to the bin covering problem, and to the weighted majority game.
What we mainly concern is how much the lack of coordination harms the social
welfare. Besides the standard PoA and PoS, which are based on Nash equilibrium,
we also take into account the strong Nash equilibrium, and several other new
equilibria. For each equilibrium, the corresponding PoA and PoS are given, and
the problems of computing an arbitrary equilibrium, as well as approximating
the best one, are also considered.Comment: 16 page
Intrinsic circle domains
Using quasiconformal mappings, we prove that any Riemann surface of finite connectivity and finite genus is conformally equivalent to an intrinsic circle domain
Ω
\Omega
in a compact Riemann surface
S
S
. This means that each connected component
B
B
of
S
∖
Ω
S\setminus \Omega
is either a point or a closed geometric disc with respect to the complete constant curvature conformal metric of the Riemann surface
(
Ω
∪
B
)
(\Omega \cup B)
. Moreover, the pair
(
Ω
,
S
)
(\Omega , S)
is unique up to conformal isomorphisms. We give a generalization to countably infinite connectivity. Finally, we show how one can compute numerical approximations to intrinsic circle domains using circle packings and conformal welding.</p
Towards More Practical Linear Programming-based Techniques for Algorithmic Mechanism Design
R. Lavy and C. Swamy (FOCS 2005, J. ACM 2011) introduced a general method for
obtaining truthful-in-expectation mechanisms from linear programming based
approximation algorithms. Due to the use of the Ellipsoid method, a direct
implementation of the method is unlikely to be efficient in practice. We
propose to use the much simpler and usually faster multiplicative weights
update method instead. The simplification comes at the cost of slightly weaker
approximation and truthfulness guarantees
Learning to Approximate a Bregman Divergence
Bregman divergences generalize measures such as the squared Euclidean
distance and the KL divergence, and arise throughout many areas of machine
learning. In this paper, we focus on the problem of approximating an arbitrary
Bregman divergence from supervision, and we provide a well-principled approach
to analyzing such approximations. We develop a formulation and algorithm for
learning arbitrary Bregman divergences based on approximating their underlying
convex generating function via a piecewise linear function. We provide
theoretical approximation bounds using our parameterization and show that the
generalization error for metric learning using our framework
matches the known generalization error in the strictly less general Mahalanobis
metric learning setting. We further demonstrate empirically that our method
performs well in comparison to existing metric learning methods, particularly
for clustering and ranking problems.Comment: 19 pages, 4 figure
Using Optimization to Obtain a Width-Independent, Parallel, Simpler, and Faster Positive SDP Solver
We study the design of polylogarithmic depth algorithms for approximately
solving packing and covering semidefinite programs (or positive SDPs for
short). This is a natural SDP generalization of the well-studied positive LP
problem.
Although positive LPs can be solved in polylogarithmic depth while using only
parallelizable iterations, the best known
positive SDP solvers due to Jain and Yao require parallelizable iterations. Several alternative solvers have
been proposed to reduce the exponents in the number of iterations. However, the
correctness of the convergence analyses in these works has been called into
question, as they both rely on algebraic monotonicity properties that do not
generalize to matrix algebra.
In this paper, we propose a very simple algorithm based on the optimization
framework proposed for LP solvers. Our algorithm only needs iterations, matching that of the best LP solver. To surmount
the obstacles encountered by previous approaches, our analysis requires a new
matrix inequality that extends Lieb-Thirring's inequality, and a
sign-consistent, randomized variant of the gradient truncation technique
proposed in
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