15,400 research outputs found
Survey-propagation decimation through distributed local computations
We discuss the implementation of two distributed solvers of the random K-SAT
problem, based on some development of the recently introduced
survey-propagation (SP) algorithm. The first solver, called the "SP diffusion
algorithm", diffuses as dynamical information the maximum bias over the system,
so that variable nodes can decide to freeze in a self-organized way, each
variable making its decision on the basis of purely local information. The
second solver, called the "SP reinforcement algorithm", makes use of
time-dependent external forcing messages on each variable, which let the
variables get completely polarized in the direction of a solution at the end of
a single convergence. Both methods allow us to find a solution of the random
3-SAT problem in a range of parameters comparable with the best previously
described serialized solvers. The simulated time of convergence towards a
solution (if these solvers were implemented on a distributed device) grows as
log(N).Comment: 18 pages, 10 figure
Asynchronous Distributed Optimization over Lossy Networks via Relaxed ADMM: Stability and Linear Convergence
In this work we focus on the problem of minimizing the sum of convex cost
functions in a distributed fashion over a peer-to-peer network. In particular,
we are interested in the case in which communications between nodes are prone
to failures and the agents are not synchronized among themselves. We address
the problem proposing a modified version of the relaxed ADMM, which corresponds
to the Peaceman-Rachford splitting method applied to the dual. By exploiting
results from operator theory, we are able to prove the almost sure convergence
of the proposed algorithm under general assumptions on the distribution of
communication loss and node activation events. By further assuming the cost
functions to be strongly convex, we prove the linear convergence of the
algorithm in mean to a neighborhood of the optimal solution, and provide an
upper bound to the convergence rate. Finally, we present numerical results
testing the proposed method in different scenarios.Comment: To appear in IEEE Transactions on Automatic Contro
Fast Discrete Consensus Based on Gossip for Makespan Minimization in Networked Systems
In this paper we propose a novel algorithm to solve the discrete consensus problem, i.e., the problem of distributing evenly a set of tokens of arbitrary weight among the nodes of a networked system. Tokens are tasks to be executed by the nodes and the proposed distributed algorithm minimizes monotonically the makespan of the assigned tasks. The algorithm is based on gossip-like asynchronous local interactions between the nodes. The convergence time of the proposed algorithm is superior with respect to the state of the art of discrete and quantized consensus by at least a factor O(n) in both theoretical and empirical comparisons
Community Detection via Semi-Synchronous Label Propagation Algorithms
A recently introduced novel community detection strategy is based on a label
propagation algorithm (LPA) which uses the diffusion of information in the
network to identify communities. Studies of LPAs showed that the strategy is
effective in finding a good community structure. Label propagation step can be
performed in parallel on all nodes (synchronous model) or sequentially
(asynchronous model); both models present some drawback, e.g., algorithm
termination is nor granted in the first case, performances can be worst in the
second case. In this paper, we present a semi-synchronous version of LPA which
aims to combine the advantages of both synchronous and asynchronous models. We
prove that our models always converge to a stable labeling. Moreover, we
experimentally investigate the effectiveness of the proposed strategy comparing
its performance with the asynchronous model both in terms of quality,
efficiency and stability. Tests show that the proposed protocol does not harm
the quality of the partitioning. Moreover it is quite efficient; each
propagation step is extremely parallelizable and it is more stable than the
asynchronous model, thanks to the fact that only a small amount of
randomization is used by our proposal.Comment: In Proc. of The International Workshop on Business Applications of
Social Network Analysis (BASNA '10
A randomized primal distributed algorithm for partitioned and big-data non-convex optimization
In this paper we consider a distributed optimization scenario in which the
aggregate objective function to minimize is partitioned, big-data and possibly
non-convex. Specifically, we focus on a set-up in which the dimension of the
decision variable depends on the network size as well as the number of local
functions, but each local function handled by a node depends only on a (small)
portion of the entire optimization variable. This problem set-up has been shown
to appear in many interesting network application scenarios. As main paper
contribution, we develop a simple, primal distributed algorithm to solve the
optimization problem, based on a randomized descent approach, which works under
asynchronous gossip communication. We prove that the proposed asynchronous
algorithm is a proper, ad-hoc version of a coordinate descent method and thus
converges to a stationary point. To show the effectiveness of the proposed
algorithm, we also present numerical simulations on a non-convex quadratic
program, which confirm the theoretical results
Scalable Peer-to-Peer Indexing with Constant State
We present a distributed indexing scheme for peer to peer networks. Past work on distributed indexing traded off fast search times with non-constant degree topologies or network-unfriendly behavior such as flooding. In contrast, the scheme we present optimizes all three of these performance measures. That is, we provide logarithmic round searches while maintaining connections to a fixed number of peers and avoiding network flooding. In comparison to the well known scheme Chord, we provide competitive constant factors. Finally, we observe that arbitrary linear speedups are possible and discuss both a general brute force approach and specific economical optimizations
On Compact Routing for the Internet
While there exist compact routing schemes designed for grids, trees, and
Internet-like topologies that offer routing tables of sizes that scale
logarithmically with the network size, we demonstrate in this paper that in
view of recent results in compact routing research, such logarithmic scaling on
Internet-like topologies is fundamentally impossible in the presence of
topology dynamics or topology-independent (flat) addressing. We use analytic
arguments to show that the number of routing control messages per topology
change cannot scale better than linearly on Internet-like topologies. We also
employ simulations to confirm that logarithmic routing table size scaling gets
broken by topology-independent addressing, a cornerstone of popular
locator-identifier split proposals aiming at improving routing scaling in the
presence of network topology dynamics or host mobility. These pessimistic
findings lead us to the conclusion that a fundamental re-examination of
assumptions behind routing models and abstractions is needed in order to find a
routing architecture that would be able to scale ``indefinitely.''Comment: This is a significantly revised, journal version of cs/050802
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