228 research outputs found
Diffusion lms strategy over wireless sensor network
The mess with distributed detection, where nodes arranged in certain topology are obliged to decideamong two speculations focused around accessible estimations.We look for completely appropriated and versatile usage, where all nodes make singular constant-choices by putting crosswise over with their quick neighbours just, and no combination focus is vital. The proffered distributed detection algorithms are based on a concept of extension of strategies that are employed for diffusion mechanism in a distributed network topology. After a large-scale systematic plan or arrangement for attaining some particular object or putting a particular idea into effect detection using diffusion LMS are fascinating in the context of sensor networksbecause of their versatility, enhanced strength to node and connection disappointment as contrasted with unified frameworks and their capability to convey vitality and correspondence assets. The proposed algorithms are inherently adaptive and can track changes in the element speculation.We examine the operation of the suggested algorithms in terms of their chances of detection and false alarm, and provide simulation results comparing with other cooperation schemes, including centralized processing and the case where there is no cooperation. In the context of digital signal processing and communication, the role of adaptive filters is very vital. In day to daywork where practical requirement is necessary,the computational complexities is the most considerable parameter in context of an adaptive filter. As it tells us about reliability of any system, agility to real time environment least mean squares (LMS) algorithm is generally utilized in light of its low computational multifaceted nature (O(N)) and easier in implementation.
Reaching an Optimal Consensus: Dynamical Systems that Compute Intersections of Convex Sets
In this paper, multi-agent systems minimizing a sum of objective functions,
where each component is only known to a particular node, is considered for
continuous-time dynamics with time-varying interconnection topologies. Assuming
that each node can observe a convex solution set of its optimization component,
and the intersection of all such sets is nonempty, the considered optimization
problem is converted to an intersection computation problem. By a simple
distributed control rule, the considered multi-agent system with
continuous-time dynamics achieves not only a consensus, but also an optimal
agreement within the optimal solution set of the overall optimization
objective. Directed and bidirectional communications are studied, respectively,
and connectivity conditions are given to ensure a global optimal consensus. In
this way, the corresponding intersection computation problem is solved by the
proposed decentralized continuous-time algorithm. We establish several
important properties of the distance functions with respect to the global
optimal solution set and a class of invariant sets with the help of convex and
non-smooth analysis
Distributed Algorithm for Continuous-type Bayesian Nash Equilibrium in Subnetwork Zero-sum Games
In this paper, we consider a continuous-type Bayesian Nash equilibrium (BNE)
seeking problem in subnetwork zero-sum games, which is a generalization of
deterministic subnetwork zero-sum games and discrete-type Bayesian zero-sum
games. In this continuous-type model, because the feasible strategy set is
composed of infinite-dimensional functions and is not compact, it is hard to
seek a BNE in a non-compact set and convey such complex strategies in network
communication. To this end, we design two steps to overcome the above
bottleneck. One is a discretization step, where we discretize continuous types
and prove that the BNE of the discretized model is an approximate BNE of the
continuous model with an explicit error bound. The other one is a communication
step, where we adopt a novel compression scheme with a designed sparsification
rule and prove that agents can obtain unbiased estimations through compressed
communication. Based on the above two steps, we propose a distributed
communication-efficient algorithm to practicably seek an approximate BNE, and
further provide an explicit error bound and an convergence
rate.Comment: Submitted to SIAM Journal on Control and Optimizatio
Shuffle SGD is Always Better than SGD: Improved Analysis of SGD with Arbitrary Data Orders
Stochastic Gradient Descent (SGD) algorithms are widely used in optimizing
neural networks, with Random Reshuffling (RR) and Single Shuffle (SS) being
popular choices for cycling through random or single permutations of the
training data. However, the convergence properties of these algorithms in the
non-convex case are not fully understood. Existing results suggest that, in
realistic training scenarios where the number of epochs is smaller than the
training set size, RR may perform worse than SGD.
In this paper, we analyze a general SGD algorithm that allows for arbitrary
data orderings and show improved convergence rates for non-convex functions.
Specifically, our analysis reveals that SGD with random and single shuffling is
always faster or at least as good as classical SGD with replacement, regardless
of the number of iterations. Overall, our study highlights the benefits of
using SGD with random/single shuffling and provides new insights into its
convergence properties for non-convex optimization
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