D-ADMM: A Communication-Efficient Distributed Algorithm for Separable Optimization

We propose a distributed algorithm, named Distributed Alternating Direction Method of Multipliers (D-ADMM), for solving separable optimization problems in networks of interconnected nodes or agents. In a separable optimization problem there is a private cost function and a private constraint set at...

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Vydané v:IEEE transactions on signal processing Ročník 61; číslo 10; s. 2718 - 2723
Hlavní autori: Mota, J. F. C., Xavier, J. M. F., Aguiar, P. M. Q., Puschel, M.
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: New York, NY IEEE 15.05.2013
Institute of Electrical and Electronics Engineers
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
Algorithm design and analysis
Algorithms
Alternating direction method of multipliers
Applied sciences
Color
Computer simulation
Constraining
Convergence
Cost function
Detection, estimation, filtering, equalization, prediction
Distributed algorithms
Exact sciences and technology
Image color analysis
Information, signal and communications theory
Networks
Optimization
Sampling, quantization
sensor networks
Sensors
Signal and communications theory
Signal processing
Signal representation. Spectral analysis
Signal, noise
Studies
Telecommunications and information theory
Alternating direction method
Alternating direction method of multipliers
Automatic classification
State of the art
Secondary cell
sensor networks
Support vector machine
Signal classification
Nodes
Optimization
Accuracy
Algorithm performance
Simulation
Interconnection
distributed algorithms
Distributed algorithm
Signal processing
Cost function
Sensor array
Private sector
Multiplying circuits
Signal detection
Compressed sensing
ISSN:1053-587X, 1941-0476
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Shrnutí:We propose a distributed algorithm, named Distributed Alternating Direction Method of Multipliers (D-ADMM), for solving separable optimization problems in networks of interconnected nodes or agents. In a separable optimization problem there is a private cost function and a private constraint set at each node. The goal is to minimize the sum of all the cost functions, constraining the solution to be in the intersection of all the constraint sets. D-ADMM is proven to converge when the network is bipartite or when all the functions are strongly convex, although in practice, convergence is observed even when these conditions are not met. We use D-ADMM to solve the following problems from signal processing and control: average consensus, compressed sensing, and support vector machines. Our simulations show that D-ADMM requires less communications than state-of-the-art algorithms to achieve a given accuracy level. Algorithms with low communication requirements are important, for example, in sensor networks, where sensors are typically battery-operated and communicating is the most energy consuming operation.
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ISSN:1053-587X
1941-0476
DOI:10.1109/TSP.2013.2254478