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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| Veröffentlicht in: | IEEE transactions on signal processing 2013-05, Vol.61 (10), p.2718-2723 |
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| container_title | IEEE transactions on signal processing |
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| creator | Mota, J. F. C. Xavier, J. M. F. Aguiar, P. M. Q. Puschel, M. |
| description | 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. |
| doi_str_mv | 10.1109/TSP.2013.2254478 |
| format | Article |
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F. C. ; Xavier, J. M. F. ; Aguiar, P. M. Q. ; Puschel, M.</creator><creatorcontrib>Mota, J. F. C. ; Xavier, J. M. F. ; Aguiar, P. M. Q. ; Puschel, M.</creatorcontrib><description>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.</description><identifier>ISSN: 1053-587X</identifier><identifier>EISSN: 1941-0476</identifier><identifier>DOI: 10.1109/TSP.2013.2254478</identifier><identifier>CODEN: ITPRED</identifier><language>eng</language><publisher>New York, NY: IEEE</publisher><subject>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. 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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.</description><subject>Algorithm design and analysis</subject><subject>Algorithms</subject><subject>Alternating direction method of multipliers</subject><subject>Applied sciences</subject><subject>Color</subject><subject>Computer simulation</subject><subject>Constraining</subject><subject>Convergence</subject><subject>Cost function</subject><subject>Detection, estimation, filtering, equalization, prediction</subject><subject>Distributed algorithms</subject><subject>Exact sciences and technology</subject><subject>Image color analysis</subject><subject>Information, signal and communications theory</subject><subject>Networks</subject><subject>Optimization</subject><subject>Sampling, quantization</subject><subject>sensor networks</subject><subject>Sensors</subject><subject>Signal and communications theory</subject><subject>Signal processing</subject><subject>Signal representation. 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F. C.</au><au>Xavier, J. M. F.</au><au>Aguiar, P. M. Q.</au><au>Puschel, M.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>D-ADMM: A Communication-Efficient Distributed Algorithm for Separable Optimization</atitle><jtitle>IEEE transactions on signal processing</jtitle><stitle>TSP</stitle><date>2013-05-15</date><risdate>2013</risdate><volume>61</volume><issue>10</issue><spage>2718</spage><epage>2723</epage><pages>2718-2723</pages><issn>1053-587X</issn><eissn>1941-0476</eissn><coden>ITPRED</coden><abstract>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.</abstract><cop>New York, NY</cop><pub>IEEE</pub><doi>10.1109/TSP.2013.2254478</doi><tpages>6</tpages></addata></record> |
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| subjects | 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 |
| title | D-ADMM: A Communication-Efficient Distributed Algorithm for Separable Optimization |
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