572,560 research outputs found
On distributed pinning observers for a network of dynamical systems
ArticleThis is the author accepted manuscript. The final version is available from IEEE via the DOI in this record.In this paper, a distributed observer structure is proposed to estimate the states of a large scale network of semi-linear systems interconnected by a positive, time varying coupling strength. The distributed observer comprises distinct sub-observers which require only local node level information and exchange their local state estimates with their ‘neighbouring’ observers. The key idea here is to use a minimum number, or at least relatively few, measurements from the network being monitored to reduce the sensor requirements. The problem is formulated as a two stage LMI optimization problem
Distributed Learning in Multi-Armed Bandit with Multiple Players
We formulate and study a decentralized multi-armed bandit (MAB) problem.
There are M distributed players competing for N independent arms. Each arm,
when played, offers i.i.d. reward according to a distribution with an unknown
parameter. At each time, each player chooses one arm to play without exchanging
observations or any information with other players. Players choosing the same
arm collide, and, depending on the collision model, either no one receives
reward or the colliding players share the reward in an arbitrary way. We show
that the minimum system regret of the decentralized MAB grows with time at the
same logarithmic order as in the centralized counterpart where players act
collectively as a single entity by exchanging observations and making decisions
jointly. A decentralized policy is constructed to achieve this optimal order
while ensuring fairness among players and without assuming any pre-agreement or
information exchange among players. Based on a Time Division Fair Sharing
(TDFS) of the M best arms, the proposed policy is constructed and its order
optimality is proven under a general reward model. Furthermore, the basic
structure of the TDFS policy can be used with any order-optimal single-player
policy to achieve order optimality in the decentralized setting. We also
establish a lower bound on the system regret growth rate for a general class of
decentralized polices, to which the proposed policy belongs. This problem finds
potential applications in cognitive radio networks, multi-channel communication
systems, multi-agent systems, web search and advertising, and social networks.Comment: 31 pages, 8 figures, revised paper submitted to IEEE Transactions on
Signal Processing, April, 2010, the pre-agreement in the decentralized TDFS
policy is eliminated to achieve a complete decentralization among player
Coded Computing for Half-Duplex Wireless Distributed Computing Systems via Interference Alignment
Distributed computing frameworks such as MapReduce and Spark are often used
to process large-scale data computing jobs. In wireless scenarios, exchanging
data among distributed nodes would seriously suffer from the communication
bottleneck due to limited communication resources such as bandwidth and power.
To address this problem, we propose a coded parallel computing (CPC) scheme for
distributed computing systems where distributed nodes exchange information over
a half-duplex wireless interference network. The CPC scheme achieves the
multicast gain by utilizing coded computing to multicast coded symbols
{intended to} multiple receiver nodes and the cooperative transmission gain by
allowing multiple {transmitter} nodes to jointly deliver messages via
interference alignment. To measure communication performance, we apply the
widely used latency-oriented metric: \emph{normalized delivery time (NDT)}. It
is shown that CPC can significantly reduce the NDT by jointly exploiting the
parallel transmission and coded multicasting opportunities. Surprisingly, when
tends to infinity and the computation load is fixed, CPC approaches zero
NDT while all state-of-the-art schemes achieve positive values of NDT. Finally,
we establish an information-theoretic lower bound for the NDT-computation load
trade-off over \emph{half-duplex} network, and prove our scheme achieves the
minimum NDT within a multiplicative gap of , i.e., our scheme is order
optimal.Comment: 17 pages, 6 figure
Distributed Optimization with Application to Power Systems and Control
In many engineering domains, systems are composed of partially independent subsystems—power systems are composed of distribution and transmission systems, teams of robots are composed of individual robots, and chemical process systems are composed of vessels, heat exchangers and reactors. Often, these subsystems should reach a common goal such as satisfying a power demand with minimum cost, flying in a formation, or reaching an optimal set-point. At the same time, limited information exchange is desirable—for confidentiality reasons but also due to communication constraints. Moreover, a fast and reliable decision process is key as applications might be safety-critical.
Mathematical optimization techniques are among the most successful tools for controlling systems optimally with feasibility guarantees. Yet, they are often centralized—all data has to be collected in one central and computationally powerful entity. Methods from distributed optimization control the subsystems in a distributed or decentralized fashion, reducing or avoiding central coordination. These methods have a long and successful history. Classical distributed optimization algorithms, however, are typically designed for convex problems. Hence, they are only partially applicable in the above domains since many of them lead to optimization problems with non-convex constraints. This thesis develops one of the first frameworks for distributed and decentralized optimization with non-convex constraints.
Based on the Augmented Lagrangian Alternating Direction Inexact Newton (ALADIN) algorithm, a bi-level distributed ALADIN framework is presented, solving the coordination step of ALADIN in a decentralized fashion. This framework can handle various decentralized inner algorithms, two of which we develop here: a decentralized variant of the Alternating Direction Method of Multipliers (ADMM) and a novel decentralized Conjugate Gradient algorithm. Decentralized conjugate gradient is to the best of our knowledge the first decentralized algorithm with a guarantee of convergence to the exact solution in a finite number of iterates. Sufficient conditions for fast local convergence of bi-level ALADIN are derived. Bi-level ALADIN strongly reduces the communication and coordination effort of ALADIN and preserves its fast convergence guarantees. We illustrate these properties on challenging problems from power systems and control, and compare performance to the widely used ADMM.
The developed methods are implemented in the open-source MATLAB toolbox ALADIN-—one of the first toolboxes for decentralized non-convex optimization. ALADIN- comes with a rich set of application examples from different domains showing its broad applicability. As an additional contribution, this thesis provides new insights why state-of-the-art distributed algorithms might encounter issues for constrained problems
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