48 research outputs found
The asymptotical error of broadcast gossip averaging algorithms
In problems of estimation and control which involve a network, efficient
distributed computation of averages is a key issue. This paper presents
theoretical and simulation results about the accumulation of errors during the
computation of averages by means of iterative "broadcast gossip" algorithms.
Using martingale theory, we prove that the expectation of the accumulated error
can be bounded from above by a quantity which only depends on the mixing
parameter of the algorithm and on few properties of the network: its size, its
maximum degree and its spectral gap. Both analytical results and computer
simulations show that in several network topologies of applicative interest the
accumulated error goes to zero as the size of the network grows large.Comment: 10 pages, 3 figures. Based on a draft submitted to IFACWC201
Real-valued average consensus over noisy quantized channels
This paper concerns the average consensus problem
with the constraint of quantized communication between
nodes. A broad class of algorithms is analyzed, in which the
transmission strategy, which decides what value to communicate
to the neighbours, can include various kinds of rounding, probabilistic
quantization, and bounded noise. The arbitrariness
of the transmission strategy is compensated by a feedback
mechanism which can be interpreted as a self-inhibitory action.
The result is that the average of the nodes state is not conserved
across iterations, and the nodes do not converge to a consensus;
however, we show that both errors can be made as small
as desired. Bounds on these quantities involve the spectral
properties of the graph and can be proved by employing
elementary techniques of LTI systems analysis
Average resistance of toroidal graphs
The average effective resistance of a graph is a relevant performance index
in many applications, including distributed estimation and control of network
systems. In this paper, we study how the average resistance depends on the
graph topology and specifically on the dimension of the graph. We concentrate
on -dimensional toroidal grids and we exploit the connection between
resistance and Laplacian eigenvalues. Our analysis provides tight estimates of
the average resistance, which are key to study its asymptotic behavior when the
number of nodes grows to infinity. In dimension two, the average resistance
diverges: in this case, we are able to capture its rate of growth when the
sides of the grid grow at different rates. In higher dimensions, the average
resistance is bounded uniformly in the number of nodes: in this case, we
conjecture that its value is of order for large . We prove this fact
for hypercubes and when the side lengths go to infinity.Comment: 24 pages, 6 figures, to appear in SIAM Journal on Control and
Optimization (SICON
Optimal strategies in the average consensus problem
We prove that for a set of communicating agents to compute the average of
their initial positions (average consensus problem), the optimal topology of
communication is given by a de Bruijn's graph. Consensus is then reached in a
finitely many steps. A more general family of strategies, constructed by block
Kronecker products, is investigated and compared to Cayley strategies.Comment: 9 pages; extended preprint with proofs of a CDC 2007 (Conference on
decision and Control) pape
Randomized Consensus with Attractive and Repulsive Links
We study convergence properties of a randomized consensus algorithm over a
graph with both attractive and repulsive links. At each time instant, a node is
randomly selected to interact with a random neighbor. Depending on if the link
between the two nodes belongs to a given subgraph of attractive or repulsive
links, the node update follows a standard attractive weighted average or a
repulsive weighted average, respectively. The repulsive update has the opposite
sign of the standard consensus update. In this way, it counteracts the
consensus formation and can be seen as a model of link faults or malicious
attacks in a communication network, or the impact of trust and antagonism in a
social network. Various probabilistic convergence and divergence conditions are
established. A threshold condition for the strength of the repulsive action is
given for convergence in expectation: when the repulsive weight crosses this
threshold value, the algorithm transits from convergence to divergence. An
explicit value of the threshold is derived for classes of attractive and
repulsive graphs. The results show that a single repulsive link can sometimes
drastically change the behavior of the consensus algorithm. They also
explicitly show how the robustness of the consensus algorithm depends on the
size and other properties of the graphs