82,652 research outputs found
Continuous-time Proportional-Integral Distributed Optimization for Networked Systems
In this paper we explore the relationship between dual decomposition and the
consensus-based method for distributed optimization. The relationship is
developed by examining the similarities between the two approaches and their
relationship to gradient-based constrained optimization. By formulating each
algorithm in continuous-time, it is seen that both approaches use a gradient
method for optimization with one using a proportional control term and the
other using an integral control term to drive the system to the constraint set.
Therefore, a significant contribution of this paper is to combine these methods
to develop a continuous-time proportional-integral distributed optimization
method. Furthermore, we establish convergence using Lyapunov stability
techniques and utilizing properties from the network structure of the
multi-agent system.Comment: 23 Pages, submission to Journal of Control and Decision, under
review. Takes comments from previous review process into account. Reasons for
a continuous approach are given and minor technical details are remedied.
Largest revision is reformatting for the Journal of Control and Decisio
Accelerating Consensus by Spectral Clustering and Polynomial Filters
It is known that polynomial filtering can accelerate the convergence towards
average consensus on an undirected network. In this paper the gain of a
second-order filtering is investigated. A set of graphs is determined for which
consensus can be attained in finite time, and a preconditioner is proposed to
adapt the undirected weights of any given graph to achieve fastest convergence
with the polynomial filter. The corresponding cost function differs from the
traditional spectral gap, as it favors grouping the eigenvalues in two
clusters. A possible loss of robustness of the polynomial filter is also
highlighted
Distributed Change Detection via Average Consensus over Networks
Distributed change-point detection has been a fundamental problem when
performing real-time monitoring using sensor-networks. We propose a distributed
detection algorithm, where each sensor only exchanges CUSUM statistic with
their neighbors based on the average consensus scheme, and an alarm is raised
when local consensus statistic exceeds a pre-specified global threshold. We
provide theoretical performance bounds showing that the performance of the
fully distributed scheme can match the centralized algorithms under some mild
conditions. Numerical experiments demonstrate the good performance of the
algorithm especially in detecting asynchronous changes.Comment: 15 pages, 8 figure
Enumerating Maximal Bicliques from a Large Graph using MapReduce
We consider the enumeration of maximal bipartite cliques (bicliques) from a
large graph, a task central to many practical data mining problems in social
network analysis and bioinformatics. We present novel parallel algorithms for
the MapReduce platform, and an experimental evaluation using Hadoop MapReduce.
Our algorithm is based on clustering the input graph into smaller sized
subgraphs, followed by processing different subgraphs in parallel. Our
algorithm uses two ideas that enable it to scale to large graphs: (1) the
redundancy in work between different subgraph explorations is minimized through
a careful pruning of the search space, and (2) the load on different reducers
is balanced through the use of an appropriate total order among the vertices.
Our evaluation shows that the algorithm scales to large graphs with millions of
edges and tens of mil- lions of maximal bicliques. To our knowledge, this is
the first work on maximal biclique enumeration for graphs of this scale.Comment: A preliminary version of the paper was accepted at the Proceedings of
the 3rd IEEE International Congress on Big Data 201
A Recoding Method to Improve the Humoral Immune Response to an HIV DNA Vaccine
This manuscript describes a novel strategy to improve HIV DNA vaccine design. Employing a new information theory based bioinformatic algorithm, we identify a set of nucleotide motifs which are common in the coding region of HIV, but are under-represented in genes that are highly expressed in the human genome. We hypothesize that these motifs contribute to the poor protein expression of gag, pol, and env genes from the c-DNAs of HIV clinical isolates. Using this approach and beginning with a codon optimized consensus gag gene, we recode the nucleotide sequence so as to remove these motifs without modifying the amino acid sequence. Transfecting the recoded DNA sequence into a human kidney cell line results in doubling the gag protein expression level compared to the codon optimized version. We then turn both sequences into DNA vaccines and compare induced antibody response in a murine model. Our sequence, which has the motifs removed, induces a five-fold increase in gag antibody response compared to the codon optimized vaccine
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